{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SAd865lNzZpT"
   },
   "source": [
    "#  <span style=\"color:orange\">Regression Tutorial (REG101) - Level Beginner</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yXN8UznszZpc"
   },
   "source": [
    "**Date Updated: Feb 25, 2020**\n",
    "\n",
    "# 1.0  Tutorial Objective\n",
    "Welcome to Regression Tutorial (REG101) - Level Beginner. This tutorial assumes that you are new to PyCaret and looking to get started with Regression using the `pycaret.regression` Module.\n",
    "\n",
    "In this tutorial we will learn:\n",
    "\n",
    "\n",
    "* **Getting Data:**  How to import data from PyCaret repository\n",
    "* **Setting up Environment:**  How to setup an experiment in PyCaret and get started with building regression models\n",
    "* **Create Model:**  How to create a model, perform cross validation and evaluate regression metrics\n",
    "* **Tune Model:**  How to automatically tune the hyper-parameters of a regression model\n",
    "* **Plot Model:**  How to analyze model performance using various plots\n",
    "* **Finalize Model:** How to finalize the best model at the end of the experiment\n",
    "* **Predict Model:**  How to make prediction on new / unseen data\n",
    "* **Save / Load Model:**  How to save / load a model for future use\n",
    "\n",
    "Read Time : Approx. 30 Minutes\n",
    "\n",
    "\n",
    "## 1.1 Installing PyCaret\n",
    "The first step to get started with PyCaret is to install pycaret. Installation is easy and will only take a few minutes. Follow the instructions below:\n",
    "\n",
    "#### Installing PyCaret in Local Jupyter Notebook\n",
    "`pip install pycaret`  <br />\n",
    "\n",
    "#### Installing PyCaret on Google Colab or Azure Notebooks\n",
    "`!pip install pycaret`\n",
    "\n",
    "\n",
    "## 1.2 Pre-Requisites\n",
    "- Python 3.x\n",
    "- Latest version of pycaret\n",
    "- Internet connection to load data from pycaret's repository\n",
    "- Basic Knowledge of Regression\n",
    "\n",
    "## 1.3 For Google colab users:\n",
    "If you are running this notebook on Google colab, run the following code at top of your notebook to display interactive visuals.<br/>\n",
    "<br/>\n",
    "`from pycaret.utils import enable_colab` <br/>\n",
    "`enable_colab()`\n",
    "\n",
    "## 1.4 See also:\n",
    "- __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/Tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__\n",
    "- __[Regression Tutorial (REG103) - Level Expert](https://github.com/pycaret/pycaret/blob/master/Tutorials/Regression%20Tutorial%20Level%20Expert%20-%20REG103.ipynb)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HuEUiXXhzZpi"
   },
   "source": [
    "# 2.0 What is Regression?\n",
    "\n",
    "Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable', or 'target') and one or more independent variables (often called 'features', 'predictors', or 'covariates'). The objective of regression in machine learning is to predict continuous values such as sales amount, quantity, temperature etc.\n",
    "\n",
    "__[Learn More about Regression](https://hbr.org/2015/11/a-refresher-on-regression-analysis)__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xnEk7n5ZzZpm"
   },
   "source": [
    "# 3.0 Overview of the Regression Module in PyCaret\n",
    "PyCaret's Regression module (`pycaret.regression`) is a supervised machine learning module which is used for predicting continuous values / outcomes using various techniques and algorithms. Regression can be used for predicting values / outcomes such as sales, units sold, temperature or any number which is continuous.\n",
    "\n",
    "PyCaret's regression module has over 25 algorithms and 10 plots to analyze the performance of models. Be it hyper-parameter tuning, ensembling or advanced techniques like stacking, PyCaret's regression module has it all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uN95Uqo6zZpq"
   },
   "source": [
    "# 4.0 Dataset for the Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Guj8GFIJzZpu"
   },
   "source": [
    "For this tutorial we will use a dataset based on a case study called **\"Sarah Gets a Diamond\"**. This case was presented in the first year decision analysis course at Darden School of Business (University of Virginia). The basis for the data is a case regarding a hopeless romantic MBA student choosing the right diamond for his bride-to-be, Sarah. The data contains 6000 records for training. Short descriptions of each column are as follows:\n",
    "\n",
    "- **ID:** Uniquely identifies each observation (diamond)\n",
    "- **Carat Weight:** The weight of the diamond in metric carats. One carat is equal to 0.2 grams, roughly the same weight as a paperclip\n",
    "- **Cut:** One of five values indicating the cut of the diamond in the following order of desirability (Signature-Ideal, Ideal, Very Good, Good, Fair)\n",
    "- **Color:** One of six values indicating the diamond's color in the following order of desirability (D, E, F - Colorless, G, H, I - Near colorless)\n",
    "- **Clarity:** One of seven values indicating the diamond's clarity in the following order of desirability (F - Flawless, IF - Internally Flawless, VVS1 or VVS2 - Very, Very Slightly Included, or VS1 or VS2 - Very Slightly Included, SI1 - Slightly Included)\n",
    "- **Polish:** One of four values indicating the diamond's polish (ID - Ideal, EX - Excellent, VG - Very Good, G - Good)\n",
    "- **Symmetry:** One of four values indicating the diamond's symmetry (ID - Ideal, EX - Excellent, VG - Very Good, G - Good)\n",
    "- **Report:** One of of two values \"AGSL\" or \"GIA\" indicating which grading agency reported the qualities of the diamond qualities\n",
    "- **Price:** The amount in USD that the diamond is valued `Target Column`\n",
    "\n",
    "\n",
    "#### Dataset Acknowledgement:\n",
    "This case was prepared by Greg Mills (MBA ’07) under the supervision of Phillip E. Pfeifer, Alumni Research Professor of Business Administration. Copyright (c) 2007 by the University of Virginia Darden School Foundation, Charlottesville, VA. All rights reserved.\n",
    "\n",
    "The original dataset and description can be __[found here.](https://github.com/DardenDSC/sarah-gets-a-diamond)__ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wwUzzm1YzZpz"
   },
   "source": [
    "# 5.0 Getting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PFCSZ_NKzZp3"
   },
   "source": [
    "You can download the data from the original source __[found here](https://github.com/DardenDSC/sarah-gets-a-diamond)__ and load it using pandas __[(Learn How)](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html)__ or you can use PyCaret's data respository to load the data using the `get_data()` function (This will require internet connection)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 191
    },
    "colab_type": "code",
    "id": "H6qS5U--zZp7",
    "outputId": "2a11a81c-7e67-425a-a3ef-091d2c9fbd30"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.10</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>5169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>VS1</td>\n",
       "      <td>ID</td>\n",
       "      <td>ID</td>\n",
       "      <td>AGSL</td>\n",
       "      <td>3470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.85</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.91</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.83</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>3171</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight    Cut Color Clarity Polish Symmetry Report  Price\n",
       "0          1.10  Ideal     H     SI1     VG       EX    GIA   5169\n",
       "1          0.83  Ideal     H     VS1     ID       ID   AGSL   3470\n",
       "2          0.85  Ideal     H     SI1     EX       EX    GIA   3183\n",
       "3          0.91  Ideal     E     SI1     VG       VG    GIA   4370\n",
       "4          0.83  Ideal     G     SI1     EX       EX    GIA   3171"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycaret.datasets import get_data\n",
    "dataset = get_data('diamond')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "D5PerU66zZqK",
    "outputId": "2fdd6ab8-7d68-4cc4-81a7-0cb82ed70799"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6000, 8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the shape of data\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7eWmeLvYzZqY"
   },
   "source": [
    "In order to demonstrate the `predict_model()` function on unseen data, a sample of 600 records has been withheld from the original dataset to be used for predictions. This should not be confused with a train/test split as this particular split is performed to simulate a real life scenario. Another way to think about this is that these 600 records are not available at the time when the machine learning experiment was performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "R4K9F7BXzZqc",
    "outputId": "22b1c4e7-a1e1-48d2-8ddc-907e716d5b53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data for Modeling: (5400, 8)\n",
      "Unseen Data For Predictions: (600, 8)\n"
     ]
    }
   ],
   "source": [
    "data = dataset.sample(frac=0.9, random_state=786).reset_index(drop=True)\n",
    "data_unseen = dataset.drop(data.index).reset_index(drop=True)\n",
    "\n",
    "print('Data for Modeling: ' + str(data.shape))\n",
    "print('Unseen Data For Predictions: ' + str(data_unseen.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DxnJV14BzZqq"
   },
   "source": [
    "# 6.0 Setting up Environment in PyCaret"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "15-blMPOzZqw"
   },
   "source": [
    "The `setup()` function initializes the environment in pycaret and creates the transformation pipeline to prepare the data for modeling and deployment. `setup()` must be called before executing any other function in pycaret. It takes two mandatory parameters: a pandas dataframe and the name of the target column. All other parameters are optional and are used to customize the pre-processing pipeline (we will see them in later tutorials).\n",
    "\n",
    "When `setup()` is executed, PyCaret's inference algorithm will automatically infer the data types for all features based on certain properties. The data type should be inferred correctly but this is not always the case. To account for this, PyCaret displays a table containing the features and their inferred data types after `setup()` is executed. If all of the data types are correctly identified `enter` can be pressed to continue or `quit` can be typed to end the expriment. Ensuring that the data types are correct is of fundamental importance in PyCaret as it automatically performs a few pre-processing tasks which are imperative to any machine learning experiment. These tasks are performed differently for each data type which means it is very important for them to be correctly configured.\n",
    "\n",
    "In later tutorials we will learn how to overwrite PyCaret's infered data type using the `numeric_features` and `categorical_features` parameters in `setup()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LgT-XURDzZqz"
   },
   "outputs": [],
   "source": [
    "from pycaret.regression import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 803
    },
    "colab_type": "code",
    "id": "7V2FN4KQzZrA",
    "outputId": "43d8d23d-ef08-438a-8cc3-ba78e9773aca",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Setup Succesfully Completed!\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Description</th>        <th class=\"col_heading level0 col1\" >Value</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row0_col0\" class=\"data row0 col0\" >session_id</td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row1_col0\" class=\"data row1 col0\" >Transform Target </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row1_col1\" class=\"data row1 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row2_col0\" class=\"data row2 col0\" >Transform Target Method</td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row2_col1\" class=\"data row2 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row3_col0\" class=\"data row3 col0\" >Original Data</td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row3_col1\" class=\"data row3 col1\" >(5400, 8)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row4_col0\" class=\"data row4 col0\" >Missing Values </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row5_col0\" class=\"data row5 col0\" >Numeric Features </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row5_col1\" class=\"data row5 col1\" >1</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row6_col0\" class=\"data row6 col0\" >Categorical Features </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row6_col1\" class=\"data row6 col1\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row7_col0\" class=\"data row7 col0\" >Ordinal Features </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row7_col1\" class=\"data row7 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row8_col0\" class=\"data row8 col0\" >High Cardinality Features </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row9_col0\" class=\"data row9 col0\" >High Cardinality Method </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row9_col1\" class=\"data row9 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row10_col0\" class=\"data row10 col0\" >Sampled Data</td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row10_col1\" class=\"data row10 col1\" >(5400, 8)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row11_col0\" class=\"data row11 col0\" >Transformed Train Set</td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row11_col1\" class=\"data row11 col1\" >(3779, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row12_col0\" class=\"data row12 col0\" >Transformed Test Set</td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row12_col1\" class=\"data row12 col1\" >(1621, 28)</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row13_col0\" class=\"data row13 col0\" >Numeric Imputer </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row13_col1\" class=\"data row13 col1\" >mean</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row14_col0\" class=\"data row14 col0\" >Categorical Imputer </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row14_col1\" class=\"data row14 col1\" >constant</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row15_col0\" class=\"data row15 col0\" >Normalize </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row16_col0\" class=\"data row16 col0\" >Normalize Method </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row16_col1\" class=\"data row16 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row17_col0\" class=\"data row17 col0\" >Transformation </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row18_col0\" class=\"data row18 col0\" >Transformation Method </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row18_col1\" class=\"data row18 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row19_col0\" class=\"data row19 col0\" >PCA </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row20_col0\" class=\"data row20 col0\" >PCA Method </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row20_col1\" class=\"data row20 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row21_col0\" class=\"data row21 col0\" >PCA Components </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row21_col1\" class=\"data row21 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row22_col0\" class=\"data row22 col0\" >Ignore Low Variance </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row22_col1\" class=\"data row22 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row23_col0\" class=\"data row23 col0\" >Combine Rare Levels </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row23_col1\" class=\"data row23 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row24_col0\" class=\"data row24 col0\" >Rare Level Threshold </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row24_col1\" class=\"data row24 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row25_col0\" class=\"data row25 col0\" >Numeric Binning </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row25_col1\" class=\"data row25 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row26_col0\" class=\"data row26 col0\" >Remove Outliers </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row26_col1\" class=\"data row26 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row27_col0\" class=\"data row27 col0\" >Outliers Threshold </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row27_col1\" class=\"data row27 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row28_col0\" class=\"data row28 col0\" >Remove Multicollinearity </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row28_col1\" class=\"data row28 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row29_col0\" class=\"data row29 col0\" >Multicollinearity Threshold </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row29_col1\" class=\"data row29 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row30_col0\" class=\"data row30 col0\" >Clustering </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row30_col1\" class=\"data row30 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row31_col0\" class=\"data row31 col0\" >Clustering Iteration </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row31_col1\" class=\"data row31 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row32_col0\" class=\"data row32 col0\" >Polynomial Features </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row32_col1\" class=\"data row32 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row33_col0\" class=\"data row33 col0\" >Polynomial Degree </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row33_col1\" class=\"data row33 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row34_col0\" class=\"data row34 col0\" >Trignometry Features </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row34_col1\" class=\"data row34 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row35_col0\" class=\"data row35 col0\" >Polynomial Threshold </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row35_col1\" class=\"data row35 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row36_col0\" class=\"data row36 col0\" >Group Features </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row36_col1\" class=\"data row36 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row37_col0\" class=\"data row37 col0\" >Feature Selection </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row37_col1\" class=\"data row37 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row38_col0\" class=\"data row38 col0\" >Features Selection Threshold </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row38_col1\" class=\"data row38 col1\" >None</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row39_col0\" class=\"data row39 col0\" >Feature Interaction </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row39_col1\" class=\"data row39 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row40_col0\" class=\"data row40 col0\" >Feature Ratio </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row40_col1\" class=\"data row40 col1\" >False</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row41_col0\" class=\"data row41 col0\" >Interaction Threshold </td>\n",
       "                        <td id=\"T_3e7d9b52_5d80_11ea_a99b_84fdd13f47d7row41_col1\" class=\"data row41 col1\" >None</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x197f39fb188>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_reg101 = setup(data = data, target = 'Price', session_id=123) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nWBypX32zZrP"
   },
   "source": [
    "Once the setup has been succesfully executed it prints the information grid which contains several important pieces of information. Most of the information is related to the pre-processing pipeline which is constructed when `setup()` is executed. The majority of these features are out of scope for the purposes of this tutorial however a few important things to note at this stage include:\n",
    "\n",
    "- **session_id :**  A pseduo-random number distributed as a seed in all functions for later reproducibility. If no `session_id` is passed, a random number is automatically generated that is distributed to all functions. In this experiment, the `session_id` is set as `123` for later reproducibility.<br/>\n",
    "<br/>\n",
    "- **Original Data :**  Displays the original shape of dataset. In this experiment (5400, 8) means 5400 samples and 8 features including the target column. <br/>\n",
    "<br/>\n",
    "- **Missing Values :**  When there are missing values in the original data this will show as True. For this experiment there are no missing values in the dataset.<br/>\n",
    "<br/>\n",
    "- **Numeric Features :**  Number of features inferred as numeric. In this dataset, 1 out of 8 features are inferred as numeric. <br/>\n",
    "<br/>\n",
    "- **Categorical Features :**  Number of features inferred as categorical. In this dataset, 6 out of 8 features are inferred as categorical. <br/>\n",
    "<br/>\n",
    "- **Transformed Train Set :** Displays the shape of the transformed training set. Notice that the original shape of (5400, 8) is transformed into (3779, 28) for the transformed train set. The number of features has increased from 8 from 28 due to categorical encoding <br/>\n",
    "<br/>\n",
    "- **Transformed Test Set :** Displays the shape of transformed test/hold-out set. There are 1621 samples in test/hold-out set. This split is based on the default value of 70/30 that can be changed using `train_size` parameter in setup. <br/>\n",
    "\n",
    "Notice how a few tasks that are imperative to perform modeling are automatically handled such as missing value imputation (in this case there are no missing values in training data, but we still need imputers for unseen data), categorical encoding etc. Most of the parameters in `setup()` are optional and used for customizing the pre-processing pipeline. These parameters are out of scope for this tutorial but as you progress to the intermediate and expert levels, we will cover them in much greater detail.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "xBqHzabEzZrT"
   },
   "source": [
    "# 7.0 Comparing All Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QHiNl6UmzZrW"
   },
   "source": [
    "Comparing all models to evaluate performance is the recommended starting point for modeling once the setup is completed (unless you exactly know what kind of model you need, which is often not the case). This function trains all models in the model library and scores them using kfold cross validation for metric evaluation. The output prints a score grid that shows average MAE, MSE, RMSE, R2, RMSLE and MAPE accross the folds (10 by default) of all the available models in the model library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "atJfMGD6zZrb",
    "outputId": "af936da0-cc93-4429-ef61-20d940e0aa4e",
    "scrolled": false
   },
   "outputs": [
    {
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       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col0 {\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col4 {\n",
       "            text-align:  left;\n",
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       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col0 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col1 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col2 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col3 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col4 {\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col5 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }    #T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col6 {\n",
       "            : ;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Model</th>        <th class=\"col_heading level0 col1\" >MAE</th>        <th class=\"col_heading level0 col2\" >MSE</th>        <th class=\"col_heading level0 col3\" >RMSE</th>        <th class=\"col_heading level0 col4\" >R2</th>        <th class=\"col_heading level0 col5\" >RMSLE</th>        <th class=\"col_heading level0 col6\" >MAPE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row0_col0\" class=\"data row0 col0\" >CatBoost Regressor</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row0_col1\" class=\"data row0 col1\" >629.397</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row0_col2\" class=\"data row0 col2\" >2.07934e+06</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row0_col3\" class=\"data row0 col3\" >1376.31</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row0_col4\" class=\"data row0 col4\" >0.9803</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row0_col5\" class=\"data row0 col5\" >0.0676</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row0_col6\" class=\"data row0 col6\" >0.0497</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col0\" class=\"data row1 col0\" >Extra Trees Regressor</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col1\" class=\"data row1 col1\" >762.012</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col2\" class=\"data row1 col2\" >2.764e+06</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col3\" class=\"data row1 col3\" >1612.24</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col4\" class=\"data row1 col4\" >0.9729</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col5\" class=\"data row1 col5\" >0.0817</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row1_col6\" class=\"data row1 col6\" >0.0607</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col0\" class=\"data row2 col0\" >Random Forest</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col1\" class=\"data row2 col1\" >760.63</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col2\" class=\"data row2 col2\" >2.92968e+06</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col3\" class=\"data row2 col3\" >1663.01</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col4\" class=\"data row2 col4\" >0.9714</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col5\" class=\"data row2 col5\" >0.0818</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row2_col6\" class=\"data row2 col6\" >0.0597</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col0\" class=\"data row3 col0\" >Light Gradient Boosting Machine</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col1\" class=\"data row3 col1\" >752.236</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col2\" class=\"data row3 col2\" >3.05557e+06</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col3\" class=\"data row3 col3\" >1687.78</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col4\" class=\"data row3 col4\" >0.9711</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col5\" class=\"data row3 col5\" >0.0773</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row3_col6\" class=\"data row3 col6\" >0.0567</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col0\" class=\"data row4 col0\" >Extreme Gradient Boosting</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col1\" class=\"data row4 col1\" >932.797</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col2\" class=\"data row4 col2\" >3.53405e+06</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col3\" class=\"data row4 col3\" >1855.33</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col4\" class=\"data row4 col4\" >0.9652</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col5\" class=\"data row4 col5\" >0.1061</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row4_col6\" class=\"data row4 col6\" >0.0801</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col0\" class=\"data row5 col0\" >Gradient Boosting Regressor</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col1\" class=\"data row5 col1\" >920.291</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col2\" class=\"data row5 col2\" >3.7643e+06</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col3\" class=\"data row5 col3\" >1901.18</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col4\" class=\"data row5 col4\" >0.9633</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col5\" class=\"data row5 col5\" >0.1024</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row5_col6\" class=\"data row5 col6\" >0.077</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col0\" class=\"data row6 col0\" >Decision Tree</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col1\" class=\"data row6 col1\" >1003.12</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col2\" class=\"data row6 col2\" >5.30562e+06</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col3\" class=\"data row6 col3\" >2228.73</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col4\" class=\"data row6 col4\" >0.9476</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col5\" class=\"data row6 col5\" >0.1083</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row6_col6\" class=\"data row6 col6\" >0.0775</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col0\" class=\"data row7 col0\" >Ridge Regression</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col1\" class=\"data row7 col1\" >2413.57</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col2\" class=\"data row7 col2\" >1.41205e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col3\" class=\"data row7 col3\" >3726.16</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col4\" class=\"data row7 col4\" >0.8621</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col5\" class=\"data row7 col5\" >0.6689</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row7_col6\" class=\"data row7 col6\" >0.2875</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col0\" class=\"data row8 col0\" >Lasso Regression</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col1\" class=\"data row8 col1\" >2412.19</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col2\" class=\"data row8 col2\" >1.42468e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col3\" class=\"data row8 col3\" >3744.23</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col4\" class=\"data row8 col4\" >0.8608</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col5\" class=\"data row8 col5\" >0.6767</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row8_col6\" class=\"data row8 col6\" >0.2866</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col0\" class=\"data row9 col0\" >Linear Regression</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col1\" class=\"data row9 col1\" >2413.55</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col2\" class=\"data row9 col2\" >1.42423e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col3\" class=\"data row9 col3\" >3744.01</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col4\" class=\"data row9 col4\" >0.8607</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col5\" class=\"data row9 col5\" >0.68</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row9_col6\" class=\"data row9 col6\" >0.2871</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col0\" class=\"data row10 col0\" >Least Angle Regression</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col1\" class=\"data row10 col1\" >2414.22</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col2\" class=\"data row10 col2\" >1.42427e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col3\" class=\"data row10 col3\" >3744.09</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col4\" class=\"data row10 col4\" >0.8607</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col5\" class=\"data row10 col5\" >0.6683</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row10_col6\" class=\"data row10 col6\" >0.2871</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col0\" class=\"data row11 col0\" >Lasso Least Angle Regression</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col1\" class=\"data row11 col1\" >2355.61</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col2\" class=\"data row11 col2\" >1.4272e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col3\" class=\"data row11 col3\" >3745.31</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col4\" class=\"data row11 col4\" >0.8607</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col5\" class=\"data row11 col5\" >0.6391</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row11_col6\" class=\"data row11 col6\" >0.2728</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col0\" class=\"data row12 col0\" >Bayesian Ridge</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col1\" class=\"data row12 col1\" >2415.8</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col2\" class=\"data row12 col2\" >1.42708e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col3\" class=\"data row12 col3\" >3746.99</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col4\" class=\"data row12 col4\" >0.8606</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col5\" class=\"data row12 col5\" >0.6696</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row12_col6\" class=\"data row12 col6\" >0.2873</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col0\" class=\"data row13 col0\" >TheilSen Regressor</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col1\" class=\"data row13 col1\" >2130.78</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col2\" class=\"data row13 col2\" >1.60278e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col3\" class=\"data row13 col3\" >3942.55</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col4\" class=\"data row13 col4\" >0.8459</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col5\" class=\"data row13 col5\" >0.4814</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row13_col6\" class=\"data row13 col6\" >0.2173</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col0\" class=\"data row14 col0\" >Random Sample Consensus</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col1\" class=\"data row14 col1\" >2023.8</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col2\" class=\"data row14 col2\" >1.64513e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col3\" class=\"data row14 col3\" >4010.84</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col4\" class=\"data row14 col4\" >0.8405</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col5\" class=\"data row14 col5\" >0.439</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row14_col6\" class=\"data row14 col6\" >0.1965</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col0\" class=\"data row15 col0\" >Huber Regressor</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col1\" class=\"data row15 col1\" >1936.05</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col2\" class=\"data row15 col2\" >1.85888e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col3\" class=\"data row15 col3\" >4251.83</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col4\" class=\"data row15 col4\" >0.821</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col5\" class=\"data row15 col5\" >0.4334</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row15_col6\" class=\"data row15 col6\" >0.1657</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col0\" class=\"data row16 col0\" >Passive Aggressive Regressor</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col1\" class=\"data row16 col1\" >1944.16</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col2\" class=\"data row16 col2\" >1.99557e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col3\" class=\"data row16 col3\" >4400.21</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col4\" class=\"data row16 col4\" >0.8083</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col5\" class=\"data row16 col5\" >0.4317</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row16_col6\" class=\"data row16 col6\" >0.1594</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col0\" class=\"data row17 col0\" >Orthogonal Matching Pursuit</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col1\" class=\"data row17 col1\" >2792.73</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col2\" class=\"data row17 col2\" >2.37287e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col3\" class=\"data row17 col3\" >4829.32</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col4\" class=\"data row17 col4\" >0.7678</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col5\" class=\"data row17 col5\" >0.5818</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row17_col6\" class=\"data row17 col6\" >0.2654</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col0\" class=\"data row18 col0\" >AdaBoost Regressor</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col1\" class=\"data row18 col1\" >4232.22</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col2\" class=\"data row18 col2\" >2.52014e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col3\" class=\"data row18 col3\" >5012.42</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col4\" class=\"data row18 col4\" >0.7467</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col5\" class=\"data row18 col5\" >0.5102</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row18_col6\" class=\"data row18 col6\" >0.597</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col0\" class=\"data row19 col0\" >K Neighbors Regressor</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col1\" class=\"data row19 col1\" >2967.05</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col2\" class=\"data row19 col2\" >2.96249e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col3\" class=\"data row19 col3\" >5421.44</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col4\" class=\"data row19 col4\" >0.7051</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col5\" class=\"data row19 col5\" >0.3663</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row19_col6\" class=\"data row19 col6\" >0.2728</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col0\" class=\"data row20 col0\" >Elastic Net</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col1\" class=\"data row20 col1\" >5029.59</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col2\" class=\"data row20 col2\" >5.63998e+07</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col3\" class=\"data row20 col3\" >7467.66</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col4\" class=\"data row20 col4\" >0.4472</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col5\" class=\"data row20 col5\" >0.5369</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row20_col6\" class=\"data row20 col6\" >0.5845</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col0\" class=\"data row21 col0\" >Support Vector Machine</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col1\" class=\"data row21 col1\" >6438.09</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col2\" class=\"data row21 col2\" >1.15316e+08</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col3\" class=\"data row21 col3\" >10710.4</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col4\" class=\"data row21 col4\" >-0.1422</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col5\" class=\"data row21 col5\" >0.7137</td>\n",
       "                        <td id=\"T_84fcf36e_5d80_11ea_a76e_84fdd13f47d7row21_col6\" class=\"data row21 col6\" >0.5281</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x197f40a6088>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compare_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "epD0BEVyzZrr"
   },
   "source": [
    "Two simple words of code ***(not even a line)*** have created over 22 models using 10 fold cross validation and evaluated the 6 most commonly use regression metrics (MAE, MSE, RMSE, R2, RMSLE and MAPE). The score grid printed above highlights the highest performing metric for comparison purposes only. The grid by default is sorted using `R2` (highest to lowest) which can be changed by passing `sort` parameter. For example `compare_models(sort = 'RMSLE')` will sort the grid by RMSLE (lower to higher since lower is better). If you want to change the fold parameter from the default value of `10` to a different value then you can use the `fold` parameter. For example `compare_models(fold = 5)` will compare all models on 5 fold cross validation. Reducing the number of folds will improve the training time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZzpBazV1zZrx"
   },
   "source": [
    "# 8.0 Create a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IPqPRp5OzZr1"
   },
   "source": [
    "While `compare_models()` is a powerful function and often a starting point in any experiment, it does not return any trained models. PyCaret's recommended experiment workflow is to use `compare_models()` right after setup to evaluate top performing models and finalize a few candidates for continued experimentation. As such, the function that actually allows to you create a model is unimaginatively called `create_model()`. This function creates a model and scores it using stratified cross validation. Similar to `compare_models()`, the output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. \n",
    "\n",
    "For the remaining part of this tutorial, we will work with the below models as our candidate models. The selections are for illustration purposes only and do not necessarily mean they are the top performing or ideal for this type of data.\n",
    "\n",
    "- AdaBoost Regressor ('ada')\n",
    "- Light Gradient Boosting Machine ('lightgbm') \n",
    "- Decision Tree\t ('dt')\n",
    "\n",
    "There are 25 regressors available in the model library of PyCaret. Please view the `create_model()` docstring for the list of all available models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wxKHHQcbzZr5"
   },
   "source": [
    "### 8.1 AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "-NVGDCR3zZr8",
    "outputId": "06f5fc68-d2a5-4b59-fea2-3661ea1cb29d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4101.8809</td>\n",
       "      <td>2.301383e+07</td>\n",
       "      <td>4797.2732</td>\n",
       "      <td>0.7473</td>\n",
       "      <td>0.4758</td>\n",
       "      <td>0.5470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4251.5693</td>\n",
       "      <td>2.929675e+07</td>\n",
       "      <td>5412.6474</td>\n",
       "      <td>0.7755</td>\n",
       "      <td>0.4940</td>\n",
       "      <td>0.5702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4047.8474</td>\n",
       "      <td>2.229166e+07</td>\n",
       "      <td>4721.4045</td>\n",
       "      <td>0.7955</td>\n",
       "      <td>0.5068</td>\n",
       "      <td>0.5871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4298.3867</td>\n",
       "      <td>2.348278e+07</td>\n",
       "      <td>4845.9038</td>\n",
       "      <td>0.7409</td>\n",
       "      <td>0.5089</td>\n",
       "      <td>0.5960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3888.5584</td>\n",
       "      <td>2.446181e+07</td>\n",
       "      <td>4945.8880</td>\n",
       "      <td>0.6949</td>\n",
       "      <td>0.4764</td>\n",
       "      <td>0.5461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4566.4889</td>\n",
       "      <td>2.973391e+07</td>\n",
       "      <td>5452.8813</td>\n",
       "      <td>0.7462</td>\n",
       "      <td>0.5462</td>\n",
       "      <td>0.6598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4628.7271</td>\n",
       "      <td>2.784109e+07</td>\n",
       "      <td>5276.4659</td>\n",
       "      <td>0.7384</td>\n",
       "      <td>0.5549</td>\n",
       "      <td>0.6676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4316.4317</td>\n",
       "      <td>2.597975e+07</td>\n",
       "      <td>5097.0336</td>\n",
       "      <td>0.6715</td>\n",
       "      <td>0.5034</td>\n",
       "      <td>0.5858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3931.2163</td>\n",
       "      <td>2.109707e+07</td>\n",
       "      <td>4593.1549</td>\n",
       "      <td>0.7928</td>\n",
       "      <td>0.4858</td>\n",
       "      <td>0.5513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4291.1097</td>\n",
       "      <td>2.481557e+07</td>\n",
       "      <td>4981.5225</td>\n",
       "      <td>0.7637</td>\n",
       "      <td>0.5495</td>\n",
       "      <td>0.6592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>4232.2217</td>\n",
       "      <td>2.520142e+07</td>\n",
       "      <td>5012.4175</td>\n",
       "      <td>0.7467</td>\n",
       "      <td>0.5102</td>\n",
       "      <td>0.5970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>233.2282</td>\n",
       "      <td>2.804219e+06</td>\n",
       "      <td>277.6577</td>\n",
       "      <td>0.0375</td>\n",
       "      <td>0.0284</td>\n",
       "      <td>0.0457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     4101.8809  2.301383e+07  4797.2732  0.7473  0.4758  0.5470\n",
       "1     4251.5693  2.929675e+07  5412.6474  0.7755  0.4940  0.5702\n",
       "2     4047.8474  2.229166e+07  4721.4045  0.7955  0.5068  0.5871\n",
       "3     4298.3867  2.348278e+07  4845.9038  0.7409  0.5089  0.5960\n",
       "4     3888.5584  2.446181e+07  4945.8880  0.6949  0.4764  0.5461\n",
       "5     4566.4889  2.973391e+07  5452.8813  0.7462  0.5462  0.6598\n",
       "6     4628.7271  2.784109e+07  5276.4659  0.7384  0.5549  0.6676\n",
       "7     4316.4317  2.597975e+07  5097.0336  0.6715  0.5034  0.5858\n",
       "8     3931.2163  2.109707e+07  4593.1549  0.7928  0.4858  0.5513\n",
       "9     4291.1097  2.481557e+07  4981.5225  0.7637  0.5495  0.6592\n",
       "Mean  4232.2217  2.520142e+07  5012.4175  0.7467  0.5102  0.5970\n",
       "SD     233.2282  2.804219e+06   277.6577  0.0375  0.0284  0.0457"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ada = create_model('ada')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NHL2zciizZsI",
    "outputId": "d606ad03-ecd5-487b-b205-bfc7a841f3a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',\n",
      "                  n_estimators=50, random_state=123)\n"
     ]
    }
   ],
   "source": [
    "#trained model object is stored in the variable 'dt'. \n",
    "print(ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-dvDHxCzZsU"
   },
   "source": [
    "### 8.2 Light Gradient Boosting Machine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "NC7OVDVrzZsX",
    "outputId": "a5abc702-d270-4134-892a-e9ecf82bdebb"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>625.1813</td>\n",
       "      <td>1.051763e+06</td>\n",
       "      <td>1025.5550</td>\n",
       "      <td>0.9885</td>\n",
       "      <td>0.0715</td>\n",
       "      <td>0.0526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>797.6185</td>\n",
       "      <td>5.638866e+06</td>\n",
       "      <td>2374.6297</td>\n",
       "      <td>0.9568</td>\n",
       "      <td>0.0727</td>\n",
       "      <td>0.0537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>825.0215</td>\n",
       "      <td>3.318727e+06</td>\n",
       "      <td>1821.7374</td>\n",
       "      <td>0.9696</td>\n",
       "      <td>0.0857</td>\n",
       "      <td>0.0616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>720.3923</td>\n",
       "      <td>1.697211e+06</td>\n",
       "      <td>1302.7707</td>\n",
       "      <td>0.9813</td>\n",
       "      <td>0.0714</td>\n",
       "      <td>0.0554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>645.6800</td>\n",
       "      <td>1.799949e+06</td>\n",
       "      <td>1341.6218</td>\n",
       "      <td>0.9775</td>\n",
       "      <td>0.0745</td>\n",
       "      <td>0.0534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>830.7176</td>\n",
       "      <td>6.423604e+06</td>\n",
       "      <td>2534.4830</td>\n",
       "      <td>0.9452</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>0.0567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>800.2611</td>\n",
       "      <td>3.355856e+06</td>\n",
       "      <td>1831.8996</td>\n",
       "      <td>0.9685</td>\n",
       "      <td>0.0793</td>\n",
       "      <td>0.0585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>714.3607</td>\n",
       "      <td>1.930223e+06</td>\n",
       "      <td>1389.3245</td>\n",
       "      <td>0.9756</td>\n",
       "      <td>0.0732</td>\n",
       "      <td>0.0556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>784.7648</td>\n",
       "      <td>2.211933e+06</td>\n",
       "      <td>1487.2569</td>\n",
       "      <td>0.9783</td>\n",
       "      <td>0.0766</td>\n",
       "      <td>0.0582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>778.3590</td>\n",
       "      <td>3.127561e+06</td>\n",
       "      <td>1768.4913</td>\n",
       "      <td>0.9702</td>\n",
       "      <td>0.0872</td>\n",
       "      <td>0.0609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>752.2357</td>\n",
       "      <td>3.055569e+06</td>\n",
       "      <td>1687.7770</td>\n",
       "      <td>0.9711</td>\n",
       "      <td>0.0773</td>\n",
       "      <td>0.0567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>68.9270</td>\n",
       "      <td>1.661228e+06</td>\n",
       "      <td>454.9486</td>\n",
       "      <td>0.0119</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.0029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     625.1813  1.051763e+06  1025.5550  0.9885  0.0715  0.0526\n",
       "1     797.6185  5.638866e+06  2374.6297  0.9568  0.0727  0.0537\n",
       "2     825.0215  3.318727e+06  1821.7374  0.9696  0.0857  0.0616\n",
       "3     720.3923  1.697211e+06  1302.7707  0.9813  0.0714  0.0554\n",
       "4     645.6800  1.799949e+06  1341.6218  0.9775  0.0745  0.0534\n",
       "5     830.7176  6.423604e+06  2534.4830  0.9452  0.0810  0.0567\n",
       "6     800.2611  3.355856e+06  1831.8996  0.9685  0.0793  0.0585\n",
       "7     714.3607  1.930223e+06  1389.3245  0.9756  0.0732  0.0556\n",
       "8     784.7648  2.211933e+06  1487.2569  0.9783  0.0766  0.0582\n",
       "9     778.3590  3.127561e+06  1768.4913  0.9702  0.0872  0.0609\n",
       "Mean  752.2357  3.055569e+06  1687.7770  0.9711  0.0773  0.0567\n",
       "SD     68.9270  1.661228e+06   454.9486  0.0119  0.0055  0.0029"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightgbm = create_model('lightgbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j8DvIuOrzZsm"
   },
   "source": [
    "### 8.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "1Y_Czm6xzZsr",
    "outputId": "df27ebb4-257b-440e-cd9b-de05ba0c0f35"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>859.1907</td>\n",
       "      <td>2.456840e+06</td>\n",
       "      <td>1567.4310</td>\n",
       "      <td>0.9730</td>\n",
       "      <td>0.1016</td>\n",
       "      <td>0.0727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1122.9409</td>\n",
       "      <td>9.852564e+06</td>\n",
       "      <td>3138.8795</td>\n",
       "      <td>0.9245</td>\n",
       "      <td>0.1102</td>\n",
       "      <td>0.0758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>911.3452</td>\n",
       "      <td>2.803663e+06</td>\n",
       "      <td>1674.4141</td>\n",
       "      <td>0.9743</td>\n",
       "      <td>0.0988</td>\n",
       "      <td>0.0729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1002.5575</td>\n",
       "      <td>3.926739e+06</td>\n",
       "      <td>1981.6002</td>\n",
       "      <td>0.9567</td>\n",
       "      <td>0.1049</td>\n",
       "      <td>0.0772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1167.8154</td>\n",
       "      <td>9.751516e+06</td>\n",
       "      <td>3122.7418</td>\n",
       "      <td>0.8784</td>\n",
       "      <td>0.1226</td>\n",
       "      <td>0.0876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1047.7778</td>\n",
       "      <td>7.833771e+06</td>\n",
       "      <td>2798.8874</td>\n",
       "      <td>0.9331</td>\n",
       "      <td>0.1128</td>\n",
       "      <td>0.0791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1010.0816</td>\n",
       "      <td>3.989282e+06</td>\n",
       "      <td>1997.3188</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.1106</td>\n",
       "      <td>0.0803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>846.8085</td>\n",
       "      <td>2.182535e+06</td>\n",
       "      <td>1477.3405</td>\n",
       "      <td>0.9724</td>\n",
       "      <td>0.0933</td>\n",
       "      <td>0.0709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1001.8451</td>\n",
       "      <td>4.904945e+06</td>\n",
       "      <td>2214.7111</td>\n",
       "      <td>0.9518</td>\n",
       "      <td>0.1053</td>\n",
       "      <td>0.0734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1060.8742</td>\n",
       "      <td>5.354348e+06</td>\n",
       "      <td>2313.9463</td>\n",
       "      <td>0.9490</td>\n",
       "      <td>0.1230</td>\n",
       "      <td>0.0847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>1003.1237</td>\n",
       "      <td>5.305620e+06</td>\n",
       "      <td>2228.7271</td>\n",
       "      <td>0.9476</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>100.2165</td>\n",
       "      <td>2.734195e+06</td>\n",
       "      <td>581.7181</td>\n",
       "      <td>0.0280</td>\n",
       "      <td>0.0091</td>\n",
       "      <td>0.0052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0      859.1907  2.456840e+06  1567.4310  0.9730  0.1016  0.0727\n",
       "1     1122.9409  9.852564e+06  3138.8795  0.9245  0.1102  0.0758\n",
       "2      911.3452  2.803663e+06  1674.4141  0.9743  0.0988  0.0729\n",
       "3     1002.5575  3.926739e+06  1981.6002  0.9567  0.1049  0.0772\n",
       "4     1167.8154  9.751516e+06  3122.7418  0.8784  0.1226  0.0876\n",
       "5     1047.7778  7.833771e+06  2798.8874  0.9331  0.1128  0.0791\n",
       "6     1010.0816  3.989282e+06  1997.3188  0.9625  0.1106  0.0803\n",
       "7      846.8085  2.182535e+06  1477.3405  0.9724  0.0933  0.0709\n",
       "8     1001.8451  4.904945e+06  2214.7111  0.9518  0.1053  0.0734\n",
       "9     1060.8742  5.354348e+06  2313.9463  0.9490  0.1230  0.0847\n",
       "Mean  1003.1237  5.305620e+06  2228.7271  0.9476  0.1083  0.0775\n",
       "SD     100.2165  2.734195e+06   581.7181  0.0280  0.0091  0.0052"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = create_model('dt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NsOBIl8szZs1"
   },
   "source": [
    "Notice that the Mean score of all models matches with the score printed in `compare_models()`. This is because the metrics printed in the `compare_models()` score grid are the average scores across all CV folds. Similar to `compare_models()`, if you want to change the fold parameter from the default value of 10 to a different value then you can use the `fold` parameter. For Example: `create_model('dt', fold = 5)` to create Decision Tree using 5 fold cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8RZB8YllzZs7"
   },
   "source": [
    "# 9.0 Tune a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AYYWC1X5zZs-"
   },
   "source": [
    "When a model is created using the `create_model()` function it uses the default hyperparameters. In order to tune hyperparameters, the `tune_model()` function is used. This function automatically tunes the hyperparameters of a model on a pre-defined search space and scores it using kfold cross validation. The output prints a score grid that shows MAE, MSE, RMSE, R2, RMSLE and MAPE by fold. <br/>\n",
    "<br/>\n",
    "**Note:** `tune_model()` does not take a trained model object as an input. It instead requires a model name to be passed as an abbreviated string similar to how it is passed in `create_model()`. All other functions in`pycaret.regression` require a trained model object as an argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5uUSmZLGzZtB"
   },
   "source": [
    "### 9.1 AdaBoost Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "XM7qgcGIzZtE",
    "outputId": "e87c7fac-4dfe-4733-ddc5-d44359b0ea0d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2755.4687</td>\n",
       "      <td>1.641891e+07</td>\n",
       "      <td>4052.0257</td>\n",
       "      <td>0.8197</td>\n",
       "      <td>0.2801</td>\n",
       "      <td>0.2603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2865.2921</td>\n",
       "      <td>2.326518e+07</td>\n",
       "      <td>4823.3988</td>\n",
       "      <td>0.8217</td>\n",
       "      <td>0.3017</td>\n",
       "      <td>0.2827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2570.6452</td>\n",
       "      <td>1.554483e+07</td>\n",
       "      <td>3942.6927</td>\n",
       "      <td>0.8574</td>\n",
       "      <td>0.2606</td>\n",
       "      <td>0.2355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2630.2358</td>\n",
       "      <td>1.475497e+07</td>\n",
       "      <td>3841.2197</td>\n",
       "      <td>0.8372</td>\n",
       "      <td>0.2719</td>\n",
       "      <td>0.2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2422.9553</td>\n",
       "      <td>1.388942e+07</td>\n",
       "      <td>3726.8506</td>\n",
       "      <td>0.8268</td>\n",
       "      <td>0.2656</td>\n",
       "      <td>0.2252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2530.4172</td>\n",
       "      <td>1.962855e+07</td>\n",
       "      <td>4430.4123</td>\n",
       "      <td>0.8325</td>\n",
       "      <td>0.2635</td>\n",
       "      <td>0.2265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2744.5501</td>\n",
       "      <td>1.711563e+07</td>\n",
       "      <td>4137.1039</td>\n",
       "      <td>0.8392</td>\n",
       "      <td>0.2936</td>\n",
       "      <td>0.2696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2820.4718</td>\n",
       "      <td>1.767699e+07</td>\n",
       "      <td>4204.4014</td>\n",
       "      <td>0.7765</td>\n",
       "      <td>0.2999</td>\n",
       "      <td>0.2817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2574.6180</td>\n",
       "      <td>1.489709e+07</td>\n",
       "      <td>3859.6744</td>\n",
       "      <td>0.8537</td>\n",
       "      <td>0.2954</td>\n",
       "      <td>0.2708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2392.6510</td>\n",
       "      <td>1.631377e+07</td>\n",
       "      <td>4039.0306</td>\n",
       "      <td>0.8447</td>\n",
       "      <td>0.2632</td>\n",
       "      <td>0.2225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>2630.7305</td>\n",
       "      <td>1.695053e+07</td>\n",
       "      <td>4105.6810</td>\n",
       "      <td>0.8309</td>\n",
       "      <td>0.2796</td>\n",
       "      <td>0.2508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>153.6911</td>\n",
       "      <td>2.620557e+06</td>\n",
       "      <td>306.4572</td>\n",
       "      <td>0.0217</td>\n",
       "      <td>0.0158</td>\n",
       "      <td>0.0233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     2755.4687  1.641891e+07  4052.0257  0.8197  0.2801  0.2603\n",
       "1     2865.2921  2.326518e+07  4823.3988  0.8217  0.3017  0.2827\n",
       "2     2570.6452  1.554483e+07  3942.6927  0.8574  0.2606  0.2355\n",
       "3     2630.2358  1.475497e+07  3841.2197  0.8372  0.2719  0.2329\n",
       "4     2422.9553  1.388942e+07  3726.8506  0.8268  0.2656  0.2252\n",
       "5     2530.4172  1.962855e+07  4430.4123  0.8325  0.2635  0.2265\n",
       "6     2744.5501  1.711563e+07  4137.1039  0.8392  0.2936  0.2696\n",
       "7     2820.4718  1.767699e+07  4204.4014  0.7765  0.2999  0.2817\n",
       "8     2574.6180  1.489709e+07  3859.6744  0.8537  0.2954  0.2708\n",
       "9     2392.6510  1.631377e+07  4039.0306  0.8447  0.2632  0.2225\n",
       "Mean  2630.7305  1.695053e+07  4105.6810  0.8309  0.2796  0.2508\n",
       "SD     153.6911  2.620557e+06   306.4572  0.0217  0.0158  0.0233"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_ada = tune_model('ada')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ul0HJFoRzZtU",
    "outputId": "5ac4ab0b-c746-4a9f-a286-3e5cd2bbe536"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoostRegressor(base_estimator=None, learning_rate=0.11, loss='exponential',\n",
      "                  n_estimators=90, random_state=123)\n"
     ]
    }
   ],
   "source": [
    "#tuned model object is stored in the variable 'tuned_dt'. \n",
    "print(tuned_ada)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3kvdvfdUzZtj"
   },
   "source": [
    "### 9.2 Light Gradient Boosting Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "s1agvmDFzZtm",
    "outputId": "7cfab1a7-e7c2-40df-ad1e-a40067bcc4e0"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>696.8135</td>\n",
       "      <td>1.223186e+06</td>\n",
       "      <td>1105.9776</td>\n",
       "      <td>0.9866</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>0.0611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>769.7567</td>\n",
       "      <td>2.878033e+06</td>\n",
       "      <td>1696.4767</td>\n",
       "      <td>0.9779</td>\n",
       "      <td>0.0740</td>\n",
       "      <td>0.0569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>796.1051</td>\n",
       "      <td>2.123431e+06</td>\n",
       "      <td>1457.1999</td>\n",
       "      <td>0.9805</td>\n",
       "      <td>0.0816</td>\n",
       "      <td>0.0629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>766.5171</td>\n",
       "      <td>1.927426e+06</td>\n",
       "      <td>1388.3177</td>\n",
       "      <td>0.9787</td>\n",
       "      <td>0.0815</td>\n",
       "      <td>0.0616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>740.2536</td>\n",
       "      <td>2.694766e+06</td>\n",
       "      <td>1641.5743</td>\n",
       "      <td>0.9664</td>\n",
       "      <td>0.0815</td>\n",
       "      <td>0.0601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>789.1642</td>\n",
       "      <td>4.094534e+06</td>\n",
       "      <td>2023.4955</td>\n",
       "      <td>0.9651</td>\n",
       "      <td>0.0805</td>\n",
       "      <td>0.0601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>720.8130</td>\n",
       "      <td>1.581897e+06</td>\n",
       "      <td>1257.7347</td>\n",
       "      <td>0.9851</td>\n",
       "      <td>0.0801</td>\n",
       "      <td>0.0603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>841.3175</td>\n",
       "      <td>3.026922e+06</td>\n",
       "      <td>1739.8053</td>\n",
       "      <td>0.9617</td>\n",
       "      <td>0.0857</td>\n",
       "      <td>0.0649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>816.6643</td>\n",
       "      <td>2.177376e+06</td>\n",
       "      <td>1475.5935</td>\n",
       "      <td>0.9786</td>\n",
       "      <td>0.0809</td>\n",
       "      <td>0.0628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>765.3249</td>\n",
       "      <td>2.960101e+06</td>\n",
       "      <td>1720.4943</td>\n",
       "      <td>0.9718</td>\n",
       "      <td>0.0933</td>\n",
       "      <td>0.0633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>770.2730</td>\n",
       "      <td>2.468767e+06</td>\n",
       "      <td>1550.6669</td>\n",
       "      <td>0.9753</td>\n",
       "      <td>0.0821</td>\n",
       "      <td>0.0614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>41.2386</td>\n",
       "      <td>7.905609e+05</td>\n",
       "      <td>253.3759</td>\n",
       "      <td>0.0081</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0     696.8135  1.223186e+06  1105.9776  0.9866  0.0816  0.0611\n",
       "1     769.7567  2.878033e+06  1696.4767  0.9779  0.0740  0.0569\n",
       "2     796.1051  2.123431e+06  1457.1999  0.9805  0.0816  0.0629\n",
       "3     766.5171  1.927426e+06  1388.3177  0.9787  0.0815  0.0616\n",
       "4     740.2536  2.694766e+06  1641.5743  0.9664  0.0815  0.0601\n",
       "5     789.1642  4.094534e+06  2023.4955  0.9651  0.0805  0.0601\n",
       "6     720.8130  1.581897e+06  1257.7347  0.9851  0.0801  0.0603\n",
       "7     841.3175  3.026922e+06  1739.8053  0.9617  0.0857  0.0649\n",
       "8     816.6643  2.177376e+06  1475.5935  0.9786  0.0809  0.0628\n",
       "9     765.3249  2.960101e+06  1720.4943  0.9718  0.0933  0.0633\n",
       "Mean  770.2730  2.468767e+06  1550.6669  0.9753  0.0821  0.0614\n",
       "SD     41.2386  7.905609e+05   253.3759  0.0081  0.0046  0.0021"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_lightgbm = tune_model('lightgbm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ovz73MkgzZtx"
   },
   "source": [
    "### 9.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "kFImcOpXzZt0",
    "outputId": "36165e01-0b2e-4fa4-efcc-5efa70a3236f"
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>874.4352</td>\n",
       "      <td>2.276756e+06</td>\n",
       "      <td>1508.8923</td>\n",
       "      <td>0.9750</td>\n",
       "      <td>0.0973</td>\n",
       "      <td>0.0740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>961.9344</td>\n",
       "      <td>5.311160e+06</td>\n",
       "      <td>2304.5955</td>\n",
       "      <td>0.9593</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.0709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>867.5361</td>\n",
       "      <td>2.470714e+06</td>\n",
       "      <td>1571.8506</td>\n",
       "      <td>0.9773</td>\n",
       "      <td>0.0947</td>\n",
       "      <td>0.0732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>996.1490</td>\n",
       "      <td>4.213594e+06</td>\n",
       "      <td>2052.7042</td>\n",
       "      <td>0.9535</td>\n",
       "      <td>0.1034</td>\n",
       "      <td>0.0747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1084.2670</td>\n",
       "      <td>8.813849e+06</td>\n",
       "      <td>2968.8127</td>\n",
       "      <td>0.8901</td>\n",
       "      <td>0.1149</td>\n",
       "      <td>0.0816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1062.5522</td>\n",
       "      <td>8.153284e+06</td>\n",
       "      <td>2855.3956</td>\n",
       "      <td>0.9304</td>\n",
       "      <td>0.1131</td>\n",
       "      <td>0.0780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>911.7975</td>\n",
       "      <td>3.759584e+06</td>\n",
       "      <td>1938.9647</td>\n",
       "      <td>0.9647</td>\n",
       "      <td>0.0957</td>\n",
       "      <td>0.0693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>975.3512</td>\n",
       "      <td>4.392592e+06</td>\n",
       "      <td>2095.8512</td>\n",
       "      <td>0.9445</td>\n",
       "      <td>0.1061</td>\n",
       "      <td>0.0763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1053.7695</td>\n",
       "      <td>4.874987e+06</td>\n",
       "      <td>2207.9373</td>\n",
       "      <td>0.9521</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.0802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>975.3018</td>\n",
       "      <td>4.146015e+06</td>\n",
       "      <td>2036.1766</td>\n",
       "      <td>0.9605</td>\n",
       "      <td>0.1176</td>\n",
       "      <td>0.0821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>976.3094</td>\n",
       "      <td>4.841254e+06</td>\n",
       "      <td>2154.1181</td>\n",
       "      <td>0.9507</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>0.0760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SD</th>\n",
       "      <td>72.1572</td>\n",
       "      <td>2.035135e+06</td>\n",
       "      <td>448.3626</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MAE           MSE       RMSE      R2   RMSLE    MAPE\n",
       "0      874.4352  2.276756e+06  1508.8923  0.9750  0.0973  0.0740\n",
       "1      961.9344  5.311160e+06  2304.5955  0.9593  0.0947  0.0709\n",
       "2      867.5361  2.470714e+06  1571.8506  0.9773  0.0947  0.0732\n",
       "3      996.1490  4.213594e+06  2052.7042  0.9535  0.1034  0.0747\n",
       "4     1084.2670  8.813849e+06  2968.8127  0.8901  0.1149  0.0816\n",
       "5     1062.5522  8.153284e+06  2855.3956  0.9304  0.1131  0.0780\n",
       "6      911.7975  3.759584e+06  1938.9647  0.9647  0.0957  0.0693\n",
       "7      975.3512  4.392592e+06  2095.8512  0.9445  0.1061  0.0763\n",
       "8     1053.7695  4.874987e+06  2207.9373  0.9521  0.1070  0.0802\n",
       "9      975.3018  4.146015e+06  2036.1766  0.9605  0.1176  0.0821\n",
       "Mean   976.3094  4.841254e+06  2154.1181  0.9507  0.1045  0.0760\n",
       "SD      72.1572  2.035135e+06   448.3626  0.0241  0.0083  0.0042"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tuned_dt = tune_model('dt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TcSjA7SUzZt-"
   },
   "source": [
    "`tune_model()` function is a random grid search of hyperparameters over a pre-defined search space. By default, it is set to optimize `R2` but this can be changed using the `optimize` parameter. For example: `tune_model('dt', optimize = 'MAE')` will search for the hyperparameters of a Decision Tree that result in the lowest `MAE` (lower is better). For the purposes of this example, we have used the default metric `R2` for the sake of simplicity only. The methodology behind selecting the right metric to evaluate a regressor is beyond the scope of this tutorial but if you would like to learn more about it, you can __[click here](https://www.dataquest.io/blog/understanding-regression-error-metrics/)__ to develop an understanding on regression error metrics. \n",
    "\n",
    "Notice how the results after tuning have been improved:\n",
    "\n",
    "- AdaBoost Regressor (Before: **`0.7467`** , After: **`0.8309`**) \n",
    "- Light Gradient Boosting Machine (Before: **`0.9711`** , After: **`0.9753`**)\n",
    "- Decision Tree (Before: **`0.9476`** , After: **`0.9507`**) \n",
    "\n",
    "Metrics alone are not the only criteria you should consider when finalizing the best model for production. Other factors to consider include training time, standard deviation of kfolds etc. As you progress through the tutorial series we will discuss those factors in detail at the intermediate and expert levels. For now, let's move forward considering the Tuned Light Gradient Boosting Machine stored in the `tuned_lightgbm` variable as our best model for the remainder of this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HR-mHgtCzZuE"
   },
   "source": [
    "# 10.0 Plot a Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "N6i7Ggg_zZuH"
   },
   "source": [
    "Before model finalization, the `plot_model()` function can be used to analyze the performance across different aspects such as Residuals Plot, Prediction Error, feature importance etc. This function takes a trained model object and returns a plot based on the test / hold-out set. \n",
    "\n",
    "There are over 10 plots available, please see the `plot_model()` docstring for the list of available plots."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HJCYRQj9zZuU"
   },
   "source": [
    "### 10.1 Residual Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 376
    },
    "colab_type": "code",
    "id": "ml-qe8dTzZuX",
    "outputId": "4c69b0b8-8e82-4003-f0e2-158b19a79f34"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rM9dWgfVzZuh"
   },
   "source": [
    "### 10.2 Prediction Error Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "GPwWRYehzZuk",
    "outputId": "e3dfe255-fa08-42f7-e556-74c7909f6e6e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm, plot = 'error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dWu_EtTGzZuu"
   },
   "source": [
    "### 10.3 Feature Importance Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7Yh852PPzZux",
    "outputId": "38295169-5000-4a07-e71d-76de9877ab42"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HERERSY6Bg4iIiCTHwEFERESSY+AgIiIiyTFwEBERkeQYOIiIiEhyDBxEREQkOQYOIiIikhwDBxEREUmOgYOIiIgkx8BBREREkjM3dAFPIy0tDcuWLcO9e/dw9+5d9OrVC1OmTIFMJiu3bWhoKAYMGICePXs+9fFCQ0ORkpICGxsbaDQa2NraYtasWWjZsuWznIbJiU7MgCo2Gam5t/GqQ2OE+irh36G1ocsiIiITYHKBo7CwEMHBwVizZg2cnZ2h1Woxbdo0REdHY/To0ZIdd8aMGfrQcu7cOUyfPh179+6V7HjGJjoxA2OjTuofJ2UX6B8zdBAR0ZOYXOCIjY2Fp6cnnJ2dAQByuRwRERFQKBRQqVRISEgAAAwcOBDjxo3T76dWqzF79mxkZmZCq9Xivffew4ABAxAQEABbW1sUFhZi48aNkMvlT6yhc+fOUCgU+Pe//41WrVpJcp4VOSFzQnxKfq0d71FLDp2vcDwiNoWBg4iInsjkAkdeXl65WxlWVlb48ccfkZWVhV27dkGj0WDMmDHw8vLSb7Nz507Y2tpi2bJlKCoqwrBhw/R/HzRoEPr27VutOuzs7HDr1q0nBo7k5ORqzVspmRNKS0prbr5qyCkoqnA8JeeWPuQ9T57HczY27IFxYB8Mz1R6YHKBw9HREampqWXGMjMzkZKSgs6dO0Mmk0GhUOC1115Denq6fpv09HR07doVAGBtbQ0XFxdkZmYCAFq3rv479Bs3bqB58+ZP3E6pVMLS0rLa81coIQGdOnWqmbmqacuxxkjKLig37tbc1mA1GUqCAftAD7AHxoF9MDxj6kFJSUmlb7JN7lMqPj4+OHHiBK5duwbgwa0SlUqFRo0a6VOeWq1GYmJimasPLi4uOHfuHACgqKgIV65cQYsWLQCgwsWmlTl16hTq1atXpcBRV4T6KiscD/F1q+VKiIjIFJncFQ5ra2uoVCqEhYVBCIHi4mL4+PggICAA2dnZGDVqFNRqNfr37w83t/++GI4cORLh4eEYPXo0SkpKEBQUBDs7uyofd9myZfjqq69gZmYGKysrrFq1SorTM1oP12lExKYgNbcArzrYIMTXjes3iIioSmRCCGHoIuqih5eWavKWijFdOnuesQ+Gxx4YB/bB8IypB0963TO5KxxSuXHjBkJCQsqNe3h4YOrUqQaoiIiIqO5g4PgPR0dHREZGGroMIiKiOsnkFo0SERGR6WHgICIiIskxcBAREZHkGDiIiIhIcgwcREREJDkGDiIiIpIcAwcRERFJjoGDiIiIJMfAQURERJJj4CAiIiLJMXAQERGR5Bg4iIiISHIMHERERCQ5Bg4iIiKSHAMHERERSY6Bg4iIiCTHwEFERESSM3jgSEtLw6RJkxAQEIDhw4dj9erViIuLw8cff1zlOYKCggAAly9fxtmzZ6tdw759+/Duu+/ivffew/jx43Hy5EkAQExMDJYvX67f7t69e/D390d6enq1j2HKohMz0H75d7CYEYX2y79DdGKGoUsiIiITY27IgxcWFiI4OBhr1qyBs7MztFotpk2bhqZNm1ZrnrVr1wIADh8+DHt7e3h4eFR53zt37mD9+vU4dOgQLCwskJubCz8/P/z0009ltktKSsJnn32G3NzcatVm6qITMzA26qT+cVJ2gf6xf4fWhiqLiIhMjEEDR2xsLDw9PeHs7AwAkMvliIiIQGJiIs6cOQMAiIqKwuHDh6HRaNCwYUOsWbMGBw8exN69e6HT6TB16lR8+umniImJwb59+6BQKODm5ob58+djz549AIDp06cjMDAQ7u7u5Wpo0KABtFotduzYAR8fH7z44os4evQozMzKXvwpLS3FunXrMHPmTGmflEqckDkhPiW/Vo+55ND5CscjYlMYOIiIqMoMGjjy8vLQsmXLMmNWVlZQKBQAAJ1Oh4KCAmzZsgVmZmaYMGECkpKSAACNGjXChg0b9Ps5ODhg6NChsLe3h7u7O+rVq4erV6/C3t4eWVlZFYYN4EHI2bx5M7Zu3YqJEydCrVbj/fffx5gxY8ps16lTp6c6x+Tk5Kfar0IyJ5SWlNbcfFWQU1BU4XhKzi0kJCTUai3G5Hk+d2PBHhgH9sHwTKUHBg0cjo6OSE1NLTOWmZmpX4dhZmYGhUKB4OBgNGjQADk5OdBoNACA1q0rf3ft5+eHmJgYODo6YvDgwY/dLjc3F/fv38fcuXMBABkZGZg4ceJTB4z/pVQqYWlpWSNzISGhxuqqqi3HGiMpu6DcuFtz21qvxVgkGKAPVBZ7YBzYB8Mzph6UlJRU+ibboItGfXx8cOLECVy7dg0AoFaroVKpYGtrCwC4dOkSjh49ilWrViE8PBw6nQ5CCAAod8sDAGQyGXQ6HQCgf//+OHXqFI4cOVJp4Pjrr7/w6aef4vbt2wAAJycn2Nra6q+yPO9CfZUVjof4utVyJUREZMoMeoXD2toaKpUKYWFhEEKguLgYPj4+cHFxwblz59CqVSvUr18fw4YNg4WFBZo2bYq8vLzHzqdUKrF06VK4uLjAy8sLHh4eyM/Ph42NzWP3cXNzw7vvvotx48ahXr160Gq18PPzQ5s2bfDbb79Jcdom5eE6jYjYFKTmFuBVBxuE+Lpx/QYREVWLTDy8ZFAHzZs3D/369YO3t3etH/vhpaWavKViTJfOnmfsg+GxB8aBfTA8Y+rBk173DHqFQ0qBgYFo1qyZPmysXbsW8fHx5bZbvHhxuYWrREREVLPqbODYtGlTmcdBQUH6LwgjIiKi2mXwbxolIiKiuo+Bg4iIiCTHwEFERESSY+AgIiIiyTFwEBERkeQYOIiIiEhyDBxEREQkOQYOIiIikhwDBxEREUmOgYOIiIgkx8BBREREkmPgICIiIskxcBAREZHkGDiIiIhIcgwcREREJDkGDiIiIpIcAwcRERFJztzQBTxOWloali1bhnv37uHu3bvo1asXunTpgp07d2LlypVVmiMoKAhr167F5cuXUVhYCA8Pj2rV0K1bN5w6dQoxMTFYvXo1WrZsqf/b+PHj4evrW635iIiInldGGTgKCwsRHByMNWvWwNnZGVqtFtOmTUPTpk2rNc/atWsBAIcPH4a9vX21A8ejBg4ciE8//fSp9zdl0YkZUMUmIzX3Nl51aIxQXyX8O7Q2dFlERGRCjPKWSmxsLDw9PeHs7AwAkMvliIiIKHOFISoqCu+++y7GjBmDDz74AKWlpYiJicHYsWMxevRonD59Gt26dUNubi727duHLVu24MKFCxgxYoR+junTp+PChQu1fXomJToxA2OjTiIpuwBanUBSdgHGRp1EdGKGoUsjIiITYpRXOPLy8sqECwCwsrKCQqEAAOh0OhQUFGDLli0wMzPDhAkTkJSUBABo1KgRNmzYoN/PwcEBQ4cOhb29Pdzd3VGvXj1cvXoV9vb2yMrKgru7e5VqOnjwIM6fPw8AsLW1xerVq2viVKvlhMwJ8Sn5tXrMJYfOVzgeEZvCqxxERFRlRhk4HB0dkZqaWmYsMzMTZ8+eBQCYmZlBoVAgODgYDRo0QE5ODjQaDQCgdevKXwT9/PwQExMDR0dHDB48uMo1Pe0tleTk5Grv81gyJ5SWlNbcfFWQU1BU4XhKzi0kJCTUai3G5Hk+d2PBHhgH9sHwTKUHRhk4fHx88OWXX2L06NF48cUXoVaroVKp0LVrVwDApUuXcPToUezevRv37t3DsGHDIIQA8CCM/C+ZTAadTgcA6N+/PzZt2gQbGxt88cUXkp+LUqmEpaVlzUyWkIBOnTrVzFxVtOVYYyRlF5Qbd2tuW+u1GIsEA/SBymIPjAP7YHjG1IOSkpJK32QbZeCwtraGSqVCWFgYhBAoLi6Gj48PXFxccO7cObRq1Qr169fHsGHDYGFhgaZNmyIvL++x8ymVSixduhQuLi7w8vKCh4cH8vPzYWNjU4tnZZpCfZUYG3Wy3HiIr5sBqiEiIlNllIEDeBAStm3bVm7cy8sLACr82/86deoUAKB3797o3bu3flyj0cDPz6/K+w8bNqwqJddJD9dpRMSmIDW3AK862CDE143rN4iIqFqMNnBIJTAwEM2aNYO3tzeABx+djY+PL7fd4sWLyy1cfV75d2jNgEFERM/kuQscmzZtKvM4KCgIQUFBBqqGiIjo+WCU38NBREREdQsDBxEREUmOgYOIiIgkx8BBREREkmPgICIiIskxcBAREZHkGDiIiIhIcgwcREREJDkGDiIiIpIcAwcRERFJjoGDiIiIJMfAQURERJJj4CAiIiLJMXAQERGR5Bg4iIiISHIMHERERCS5KgWOCxcuYPPmzSgtLUVgYCC8vLxw/PhxqWsjIiKiOqJKgWPhwoVo27YtfvjhB9SrVw/79u3DF198IXVtREREVEdUKXDodDp0794dP/30E9544w288MIL0Gq1T3XAtLQ0TJo0CQEBARg+fDhWr16NuLg4fPzxx1WeIygoCABw+fJlnD17tlrHnzlzJvbs2VNmbMuWLVi5ciXu37+P0NBQBAYGYsKECZg2bRpu3bql3+7evXvw9/dHenp6tY5pCqITM9B++XewmBGF9su/Q3RihqFLIiKiOqRKgaN+/frYtGkT4uLi4OPjg23btsHKyqraByssLERwcDBmz56NyMhI7Nq1C1euXEFGRvVe3NauXQsAOHz4MK5evVqtfUeOHIn9+/eXGdu3bx/8/Pywd+9e2NvbY9OmTdi4cSM6dOiAdevWAQCSkpIwduxYZGZmVut4piA6MQNjo04iKbsAWp1AUnYBxkadZOggIqIaY16VjZYvX47du3dj7dq1aNy4MXJzc7FixYpqHyw2Nhaenp5wdnYGAMjlckRERCAxMRFnzpwBAERFReHw4cPQaDRo2LAh1qxZg4MHD2Lv3r3Q6XSYOnUqPv30U8TExGDfvn1QKBRwc3PD/Pnz9Vcupk+fjsDAQLi7u5eroXPnzsjPz8f169fh5OSECxcuwN7eHi1atICTkxP27NmDjh07okuXLggICIAQAgBQWlqKdevWYebMmdU+75pyQuaE+JT8Gp93yaHzFY5HxKbAv0PrGj8eERE9f6oUOBwcHODl5YVLly7Bzc0NvXv3RvPmzat9sLy8PLRs2bLMmJWVFRQKBYAHt24KCgqwZcsWmJmZYcKECUhKSgIANGrUCBs2bChT09ChQ2Fvbw93d3fUq1cPV69ehb29PbKysioMGw+NGDECBw4cwOTJkxETEwN/f38AQO/evVFaWoo9e/Zg1qxZcHV1RVhYGNq1a4dOnTpV+3wBIDk5+an2q5DMCaUlpTU333/kFBRVOJ6ScwsJCQk1fry6gM+L4bEHxoF9MDxT6UGVAsfWrVtx9OhR5OXloX///pg7dy5GjBiBCRMmVOtgjo6OSE1NLTOWmZmpX4dhZmYGhUKB4OBgNGjQADk5OdBoNACA1q0rf6ft5+eHmJgYODo6YvDgwZVuO2TIEIwfPx6BgYE4c+YMwsLCAACJiYnw9vbGG2+8Aa1Wi/3792PWrFmIiYmp1nk+SqlUwtLS8qn3LyMh4amDT2W2HGuMpOyCcuNuzW0lOZ6pS5CoD1R17IFxYB8Mz5h6UFJSUumb7Cqt4di3bx82btyI+vXrw9bWFnv27MHevXurXYyPjw9OnDiBa9euAQDUajVUKhVsbW0BAJcuXcLRo0exatUqhIeHQ6fT6W9pmJmVL1Umk0Gn0wEA+vfvj1OnTuHIkSNPDBxNmjSBi4sL1q9fj759+8Lc/EHuOnToEL7++msAD273tGvXDhYWFtU+T1MT6quscDzE162WKyEiorqqSlc4zMzMyrzwWlpaQi6XV/tg1tbWUKlUCAsLgxACxcXF8PHxgYuLC86dO4dWrVqhfv36GDZsGCwsLNC0aVPk5eU9dj6lUomlS5fCxcUFXl5e8PDwQH5+PmxsbJ5Yy8iRI/H+++/jX//6l35s+vTpWLBgAYYMGYL69eujQYMGWLRoUbXP09Q8XKcREZuC1NwCvOpggxBfN67fICKiGlOlwNGlSxdERETg3r17OHr0KHbu3AkvL6+nOqBSqcS2bdvKjT+cr6K//a9Tp04BeLDmonfv3vpxjUYDPz+/KtXh7e1d7tKPtbU1IiIiKt0vMohJ0XoAACAASURBVDKySvObGv8OrRkwiIhIMlUKHDNnzsSuXbvQrl07fPvtt+jVq5d+oaWxCAwMRLNmzeDt7Q3gwUdn4+Pjy223ePHicgtXiYiISFpVChzvv/8+Nm7caHQh41GbNm0q8zgoKEj/BWFERERkWFVaNHrv3j1kZ2dLXQsRERHVUVW6wpGfn48+ffrAzs4OlpaWEEJAJpMhNjZW6vqIiIioDqhS4Ni4caPUdRAREVEdVqXA8bgfSHNycqrRYoiIiKhuqlLgePTTHmq1GgkJCejcuTPefvttyQojIiKiuqNKgWPJkiVlHhcUFFTr5+SJiIjo+ValT6n8rwYNGuD69es1XQsRERHVUVW6whEQEACZTAYAEEIgKysLPXv2lLQwIiIiqjuqFDimTJmi/7dMJoOtrS1eeuklyYoiIiKiuqVKt1R++OEHdOnSBV26dIGHhwdeeuklhISESF0bERER1RGVXuGYM2cOMjMzkZycjLS0NP24RqPBnTt3JC+OiIiI6oZKA8fkyZNx/fp1LFq0qMzvksjlcri4uEheHBEREdUNlQaOFi1aoEWLFjhw4AAKCgpw7949CCGg1Wpx8eJF/S+zEhEREVWmSotG16xZgy1btkCj0cDGxgZ5eXlQKpXYvXu31PURERFRHVClRaP79u3Dzz//jAEDBiAyMhIbNmyAra2t1LURERFRHVGlwNGsWTNYW1ujbdu2uHTpEnr37s2fqyciIqIqq9ItFWtra3z77bdwc3NDVFQUmjVrhvv370tdGxEREdURVbrCsWjRIuTn58PT0xNOTk6YO3cupk+fLnVtREREVEdUKXA4ODjA398fly5dwsyZMxEdHY233npL0sLS0tIwadIkBAQEYPjw4Vi9ejWEEBVue+PGDRw7dqzS+fr06YOSkpIyY+np6QgICKh2bQEBAUhPT6/2fsYqOjED7Zd/B4sZUWi//DtEJ2YYuiQiIqpjqhQ4Tp8+jSFDhuBvf/sbbt68iT59+uDkyZOSFVVYWIjg4GDMnj0bkZGR2LVrF65cuYLo6OgKt4+Li8Ovv/4qWT11WXRiBsZGnURSdgG0OoGk7AKMjTrJ0EFERDWqSms4Pv/8c3zzzTd4//330bRpU0RFRSE4OBjdu3eXpKjY2Fh4enrC2dkZwIMvGouIiEBiYiI+/vhjrFy5EgDQrVs3HD9+HP/4xz9w//59dOjQAb6+vpXOnZeXh08//RRCCDRt2lQ/fubMGaxcuRJyuRwtW7bE/PnzUVJSgjlz5uDOnTu4desW/Pz8MGbMGEnOuSpOyJwQn5Jfo3MuOXS+wvGI2BT4d2hdo8ciIqLnV5UCh06nK/PiLPUPt+Xl5aFly5ZlxqysrKBQKMptK5fLMWnSJPz+++9PDBsAsHnzZgwcOBAjR47EP//5T+zYsQNCCISHh+Obb76BnZ0dVq1ahX379sHNzQ1vvfUW3njjDeTm5iIgIKDagSM5Obla21dK5oTSktKamw9ATkFRheMpObeQkJBQo8eqS/jcGB57YBzYB8MzlR5UKXA0b94cP/74I2QyGQoLC7F9+3Y4OjpKVpSjoyNSU1PLjGVmZuLs2bNlxh63pqMyaWlpGDJkCACgY8eO2LFjB/Lz85GXl6dfCHv//n1069YNvXr1wtatW3H48GFYW1tDo9FU+3hKpRKWlpbV3q9CCQno1KlTzcz1H1uONUZSdkG5cbfmtjV+rLoiQYI+UPWwB8aBfTA8Y+pBSUlJpW+yK13DkZubCwCYP38+vvvuO2RnZ6Nv3764ePEi5s+fX7OVPsLHxwcnTpzAtWvXAABqtRoqlQrm5ub4888/AQDXr1/H7du3H5yEmRl0Ol2V5m7Tpg0SExMBAElJSQAAW1tbNG/eHOvXr0dkZCQ+/PBDeHp6YtOmTWjfvj2WL1+O/v37P1XAMXahvsoKx0N83Wq5EiIiqssqvcLx4YcfYt++fbCzs4NSqcTnn39eK0VZW1tDpVIhLCwMQggUFxfDx8cHEyZMwPnz5+Hn5wcXFxe0aNECAODq6ooNGzbob4FUZtq0afj444/xz3/+U7+/mZkZ5syZg0mTJkEIASsrKyxduhQymQzz5s3Dd999BxsbG8jlcpSW1uwtDUN7uE4jIjYFqbkFeNXBBiG+bly/QURENUomKnnb/vbbb+Pbb78FAAwdOhT79u2rtcJM3cNLSzV5S8WYLp09z9gHw2MPjAP7YHjG1IMnve5VeoVDJpPp/20KtxN27tyJgwcPlhsPDg5Ghw4dDFARERERAVVcNAqUDR/GatSoURg1apShyyAiIqL/UWngSEtL03/UNDc3V/9vIQRkMhliY2Olr5CIiIhMXqWB44cffqitOoiIiKgOqzRwODk51VYdREREVIdV6bdUiIiIiJ4FAwcRERFJjoGDiIiIJMfAQURERJJj4CAiIiLJMXAQERGR5Bg4iIiISHIMHERERCQ5Bg4iIiKSHAMHERERSY6Bg4iIiCTHwEFERESSY+AgIiIiyTFwEBERkeQq/Xl6qruiEzOgik1Gau5tvOrQGKG+Svh3aG3osoiIqI4yySscaWlpmDRpEgICAjB8+HCsXr0aQogKtw0NDcXx48ef6XgVzdGtW7dnmtOQohMzMDbqJJKyC6DVCSRlF2Bs1ElEJ2YYujQiIqqjTO4KR2FhIYKDg7FmzRo4OztDq9Vi2rRpiI6OxujRow1dnqROyJwQn5L/zPMsOXS+wvGI2BRe5SAiIkmYXOCIjY2Fp6cnnJ2dAQByuRwRERFQKBRQqVRISEgAAAwcOBDjxo3T76dWqzF79mxkZmZCq9Xivffew4ABAxAQEABbW1sUFhZi48aNkMvlNVpvcnJyzU0mc0JpSekzT5NTUFTheErOLf3zR5Xj82R47IFxYB8Mz1R6YHKBIy8vDy1btiwzZmVlhR9//BFZWVnYtWsXNBoNxowZAy8vL/02O3fuhK2tLZYtW4aioiIMGzZM//dBgwahb9++lR532bJl+Oqrr/SPb9++XaV6lUolLC0tq3p6lUtIQKdOnZ55mi3HGiMpu6DcuFtz2xqZv65LqKE+0NNjD4wD+2B4xtSDkpKSSt9km1zgcHR0RGpqapmxzMxMpKSkoHPnzpDJZFAoFHjttdeQnp6u3yY9PR1du3YFAFhbW8PFxQWZmZkAgNatn3wbYcaMGejZs6f+sSmv4Qj1VWJs1Mly4yG+bgaohoiIngcmt2jUx8cHJ06cwLVr1wA8uFWiUqnQqFEj/WUltVqNxMREtGrVSr+fi4sLzp07BwAoKirClStX0KJFCwCATCar5bMwLP8OrbH9ne5wf8EW5mYyuL9gi+3vdOf6DSIikozJXeGwtraGSqVCWFgYhBAoLi6Gj48PAgICkJ2djVGjRkGtVqN///5wc/vvO/aRI0ciPDwco0ePRklJCYKCgmBnZ2fAMzEs/w6tGTCIiKjWyMTjPk9Kz+ThvayaXMNhTPfqnmfsg+GxB8aBfTA8Y+rBk173TO4Kh1Ru3LiBkJCQcuMeHh6YOnWqASoiIiKqOxg4/sPR0RGRkZGGLoOIiKhOMrlFo0RERGR6GDiIiIhIcgwcREREJDkGDiIiIpIcAwcRERFJjoGDiIiIJMfAQURERJJj4CAiIiLJMXAQERGR5Bg4iIiISHIMHERERCQ5Bg4iIiKSHAMHERERSY6Bg4iIiCTHwEFERESSY+AgIiIiyZnX9gHT0tKwbNky3Lt3D3fv3kWvXr3QpUsX7Ny5EytXrqzSHEFBQVi7di0uX76MwsJCeHh4VPn4M2fORJcuXTBixAj92JYtW3Dr1i1MnjwZ8+bNQ15eHmQyGaytrTFv3jzY2tri4MGD2Lp1K+RyOVxdXTFv3jyYmZluXotOzIAqNhmpubfxqkNjhPoq4d+htaHLIiKiOqpWXzELCwsRHByM2bNnIzIyErt27cKVK1eQkZFRrXnWrl0LADh8+DCuXr1arX1HjhyJ/fv3lxnbt28f/Pz8sHfvXtjb22PTpk3YuHEjOnTogHXr1uH+/ftYtWoVtm3bhujoaBQVFeHHH3+s1nGNSXRiBsZGnURSdgG0OoGk7AKMjTqJ6MTq9YGIiKiqavUKR2xsLDw9PeHs7AwAkMvliIiIQGJiIs6cOQMAiIqKwuHDh6HRaNCwYUOsWbMGBw8exN69e6HT6TB16lR8+umniImJwb59+6BQKODm5ob58+djz549AIDp06cjMDAQ7u7u5Wro3Lkz8vPzcf36dTg5OeHChQuwt7dHixYt4OTkhD179qBjx47o0qULAgICIISAmZkZoqOjUb9+fQCARqOBpaVl7Txpjzghc0J8Sv4zz7Pk0PkKxyNiU3iVg4iIJFGrgSMvLw8tW7YsM2ZlZQWFQgEA0Ol0KCgowJYtW2BmZoYJEyYgKSkJANCoUSNs2LBBv5+DgwOGDh0Ke3t7uLu7o169erh69Srs7e2RlZVVYdh4aMSIEThw4AAmT56MmJgY+Pv7AwB69+6N0tJS7NmzB7NmzYKrqyvCwsLQrl072NvbAwAiIyNx9+5ddOvWrUrnnJycXPUn6ElkTigtKX3maXIKiiocT8m5hYSEhGee/3nA58nw2APjwD4Ynqn0oFYDh6OjI1JTU8uMZWZm4uzZswAAMzMzKBQKBAcHo0GDBsjJyYFGowEAtG5d+TtvPz8/xMTEwNHREYMHD6502yFDhmD8+PEIDAzEmTNnEBYWBgBITEyEt7c33njjDWi1Wuzfvx+zZs1CTEwMdDodli1bhoyMDKxZswYymaxK56xUKmvuakhCAjp16vTM02w51hhJ2QXlxt2a29bI/HVdQg31gZ4ee2Ac2AfDM6YelJSUVPomu1bXcPj4+ODEiRO4du0aAECtVkOlUsHW1hYAcOnSJRw9ehSrVq1CeHg4dDodhBAPCq1ggaZMJoNOpwMA9O/fH6dOncKRI0eeGDiaNGkCFxcXrF+/Hn379oW5+YPcdejQIXz99dcAHtzuadeuHSwsLAAAc+fORUlJCdavX6+/tWKqQn2VFY6H+LrVciVERPS8qNUrHNbW1lCpVAgLC4MQAsXFxfDx8YGLiwvOnTuHVq1aoX79+hg2bBgsLCzQtGlT5OXlPXY+pVKJpUuXwsXFBV5eXvDw8EB+fj5sbGyeWMvIkSPx/vvv41//+pd+bPr06ViwYAGGDBmC+vXro0GDBli0aBFSUlKwZ88edO7cGePGjQMAvPvuu+jbt++zPykG8HCdRkRsClJzC/Cqgw1CfN24foOIiCRT6x+LVSqV2LZtW7lxLy8vAKjwb//r1KlTAB6suejdu7d+XKPRwM/Pr0p1eHt7l7v0Y21tjYiIiAq3v3TpUpXmNRX+HVozYBARUa2p9cAhlcDAQDRr1gze3t4AHnx0Nj4+vtx2ixcvLrdwlYiIiKRVZwLHpk2byjwOCgpCUFCQgaohIiKiR5nuV2USERGRyWDgICIiIskxcBAREZHkGDiIiIhIcgwcREREJDkGDiIiIpIcAwcRERFJjoGDiIiIJMfAQURERJJj4CAiIiLJMXAQERGR5Bg4iIiISHIMHERERCQ5Bg4iIiKSHAMHERERSY6Bg4iIiCTHwEFERESSY+AgIiIiyZkbugCqfdGJGVDFJiM19zZedWiMUF8l/Du0NnRZRERUhxnNFY74+Hh4e3sjICAAAQEBGDlyJCIjIyvcNisrCyNHjgQAfPzxxygtLa1wuzVr1mDHjh1PdfyAgABMnToVADBjxgysX79ev+3u3bvxySefVOf0jEZ0YgbGRp1EUnYBtDqBpOwCjI06iejEDEOXRkREdZhRXeHw8vLCypUrAQClpaXo378/hgwZgkaNGj12n4fb1/TxH/XZZ59hxIgR8PLygqWlJb755hts3769xo5bVSdkTohPyX+mOZYcOl/heERsCq9yEBGRZIwqcDyqqKgIZmZmuHLlClasWAG5XA5LS0ssWLCgzHZ9+vTB999/j59//hlfffUVzM3N4eTkhKVLlwIAYmNj8a9//QsFBQWYNm0a+vTpU+1arK2tsXTpUsyaNQsWFhZYtmwZGjRoUKV9k5OTq328x5I5obSk4qs5VZVTUFTheErOLSQkJDzT3M8TPleGxx4YB/bB8EylB0YVOOLi4hAQEACZTAaFQoHw8HAsXrwYixYtwiuvvIKjR49CpVJh5syZ5fY9ePAgxo8fj7feegvffvstiooevLA6ODhg0aJFiI+Px9dff11p4Hh4/Id69eqFiRMnAgDc3d3RuHFj1KtXDy+99FKVz0mpVMLS0rLK21cqIQGdOnV6pim2HGuMpOyCcuNuzW2fee7nRUIN9IGeDXtgHNgHwzOmHpSUlFT6JtuoAkdFtzTmzJmDV155BQDg4eGBFStWVLjvrFmz8OWXX2LHjh1o06YNXn/9dQCAm5sbAMDe3h7379+v9vEf2rZtG5ycnJCfn4+dO3di1KhR1To3YxHqq8TYqJPlxkN83QxQDRERPS+MKnBUpFmzZrh06RJefvllnD17Fs7OzhVut3PnTkyZMgV2dnaYO3cujhw5AgCQyWTPXMOFCxewc+dO7Ny5E3fv3sWoUaPQsWNHtG3b9pnnrm0P12lExKYgNbcArzrYIMTXjes3iIhIUkYfOBYuXIgFCxZACAG5XI7FixdXuJ27uzvee+892NjYwMrKCr1790ZUVFS1jvW/t1QAYPny5ZgxYwZWrFgBa2trWFtbIzw8HMHBwdi9ezfq1av31OdmKP4dWjNgEBFRrZIJIYShi6iLHt7Lqsk1HMZ0r+55xj4YHntgHNgHwzOmHjzpdc/or3DUtHnz5iE9Pb3c+FdffWWSVyuIiIhMwXMZOIiIiKh2Gc03jRIREVHdxcBBREREkmPgICIiIskxcBAREZHkGDiIiIhIcgwcREREJDkGDiIiIpIcAwcRERFJjoGDiIiIJMfAQURERJJj4CAiIiLJMXAQERGR5Bg4iIiISHIMHERERCQ5Bg4iIiKSHAMHERERSc7c0AVQ7YlOzIAqNhmpubfxqkNjhPoq4d+htaHLIiKi54BJBo60tDQsW7YM9+7dw927d9GrVy9MmTIFMpms3LahoaEYMGAAevbs+dTHCw0NRUpKCmxsbPRjgwcPhp+f31PPWduiEzMwNuqk/nFSdoH+MUMHERFJzeQCR2FhIYKDg7FmzRo4OztDq9Vi2rRpiI6OxujRoyU77owZM54ptNSEEzInxKfkP9W+Sw6dr3A8IjaFgYOIiCRncoEjNjYWnp6ecHZ2BgDI5XJERERAoVBApVIhISEBADBw4ECMGzdOv59arcbs2bORmZkJrVaL9957DwMGDEBAQABsbW1RWFiIjRs3Qi6X12i9ycnJNTeZzAmlJaVPtWtOQVGF4yk5t/TPGVUdnzPDYw+MA/tgeKbSA5MLHHl5eWjZsmWZMSsrK/z444/IysrCrl27oNFoMGbMGHh5eem32blzJ2xtbbFs2TIUFRVh2LBh+r8PGjQIffv2rfS4y5Ytw1dffaV/HBYWhnbt2j2xXqVSCUtLy+qc4uMlJKBTp05PteuWY42RlF1Qbtytue1Tz/m8SniGPlDNYA+MA/tgeMbUg5KSkkrfZJtc4HB0dERqamqZsczMTKSkpKBz586QyWRQKBR47bXXkJ6ert8mPT0dXbt2BQBYW1vDxcUFmZmZAIDWrZ98S8EYbqk8i1BfZZk1HA+F+LoZoBoiInremNzHYn18fHDixAlcu3YNwINbJSqVCo0aNdJfVlKr1UhMTESrVq30+7m4uODcuXMAgKKiIly5cgUtWrQAgAoXm9Y1/h1aY/s73eH+gi3MzWRwf8EW29/pzvUbRERUK0zuCoe1tTVUKhXCwsIghEBxcTF8fHwQEBCA7OxsjBo1Cmq1Gv3794eb23/fvY8cORLh4eEYPXo0SkpKEBQUBDs7uyof939vqXh4eGDq1Kk1em5S8+/QmgGDiIgMwuQCB/BgXcS2bdvKjYeEhJQbU6lU+n9HRESU+3tkZOQTj/foHERERFR9Jhk4pHDjxo0KA4spXskgIiIyNgwc/+Ho6Filqx1ERERUfSa3aJSIiIhMDwMHERERSY6Bg4iIiCTHwEFERESS46JRiQghAAClpU/32yePU1JSUqPz0dNhHwyPPTAO7IPhGUsPHr7ePXz9+18y8bi/0DO5c+cOrly5YugyiIiIapWrqysaNmxYbpyBQyI6nQ7FxcVQKBTPxVenExHR800IAbVaDSsrK5iZlV+xwcBBREREkuOiUSIiIpIcAwcRERFJjoGDiIiIJMfAQURERJLj93CYAJ1Oh3nz5uHy5cuwsLDAwoUL0apVK0OXVaedP38ey5cvR2RkJP79738jNDQUMpkMbdu2xWeffQYzMzOsXbsWP/30E8zNzTF79my4u7sbuuw6Q61WY/bs2bh+/TpKS0sxefJkvPTSS+xDLdNqtQgLC0NGRgbkcjmWLFkCIQT7YAA3b97EsGHDsGnTJpibm5tmDwQZvR9++EGEhIQIIYRITEwUH374oYErqtv+8Y9/iIEDBwo/Pz8hhBAffPCBiIuLE0IIER4eLg4fPiySk5NFQECA0Ol04vr162LYsGGGLLnO2bNnj1i4cKEQQoj8/HzRq1cv9sEAjhw5IkJDQ4UQQsTFxYkPP/yQfTCA0tJS8be//U288cYb4urVqybbA95SMQEJCQno0aMHAKB9+/ZITk42cEV124svvog1a9boH6ekpKBLly4AgJ49e+KXX35BQkICunfvDplMBkdHR2i1WuTn5xuq5Dqnf//+mDZtmv6xXC5nHwzg9ddfx4IFCwAAN27cgL29PftgABEREfD390ezZs0AmO5/kxg4TEBRURGsra31j+VyOTQajQErqtv69esHc/P/3m0UQui/vM3Kygp37twp15OH41QzrKysYG1tjaKiIkydOhXTp09nHwzE3NwcISEhWLBgAfr168c+1LKYmBg0adJE/6YTMN3/JjFwmABra2sUFxfrH+t0ujIviCStR78xr7i4GI0aNSrXk+Li4gq/ypeeXnZ2Nt59910MGTIEgwYNYh8MKCIiAj/88APCw8PL/G4H+yC9vXv34pdffkFAQAAuXryIkJCQMlcuTKkHDBwmoGPHjjh+/DgA4LfffoOrq6uBK3q+vPrqq4iPjwcAHD9+HJ07d0bHjh1x8uRJ6HQ63LhxAzqdDk2aNDFwpXXHX3/9hcDAQMyYMQMjRowAwD4Ywrfffosvv/wSAFC/fn3IZDIolUr2oRZt374dUVFRiIyMxCuvvIKIiAj07NnTJHvAt8kmoG/fvjh16hT8/f0hhMDixYsNXdJzJSQkBOHh4fj888/Rpk0b9OvXD3K5HJ07d8aoUaOg0+kwd+5cQ5dZp/y///f/UFhYiPXr12P9+vUAgDlz5mDhwoXsQy164403MGvWLIwdOxYajQazZ8+Gi4sL//dgYKb63yT+lgoRERFJjrdUiIiISHIMHERERCQ5Bg4iIiKSHAMHERERSY6Bg4iIiCTHwEFEZYwePRqHDh0qM3b37l14enpW66uShwwZUunfY2JiEBoaWm48KysLffr0qfJx4uPjERAQUOXtn9WdO3fw0Ucf1drxiOoKBg4iKmP48OH47rvvyowdPnwYnp6e1foiof3799d0aUbh9u3buHjxoqHLIDI5/OIvIirjzTffxNKlS1FQUAAbGxsAwIEDBzBu3DgAwPfff4/Nmzfj/v37KC0txeLFi9GxY0cEBASgcePGSEtLw6pVq/D222/j8uXLyM3NxezZs3Hnzh3k5eVh6NCh+h9m+/e//42xY8fi9u3b6N27Nz755JMytfz111+YO3cucnJyIJPJ8Mknn6Br166PrX3NmjW4ceMG/vjjD+Tn52Py5Mk4ffo0zp8/j5dffhkrV67EmTNnsH79epibmyMrKwvu7u5YtGgRLCwssHfvXmzevBkymQxubm4IDw+HlZUVvLy8oFQq8eeff6Jp06bIy8vDRx99hHXr1mHlypU4ffo0bt++jWbNmmHlypWwt7dH9+7d0a9fPyQkJEAul2PVqlVo2bIlfvnlF6hUKggh4OjoiBUrVqB+/fpYunQpzpw5A61Wi2HDhmH8+PHSNJjIUAz3Q7VEZKxCQ0PFjh07hBBC5OTkiN69ewuNRiO0Wq149913xc2bN4UQQuzevVt88MEHQggh3nnnHbF69Wr9HK6urkIIIb7++msRExMjhBCisLBQdOjQQdy8eVPs3btXdO/eXdy8eVOUlJQIf39/ceTIEZGZmSl8fHyEEEJMnz5dHD16VAghRG5urvD19RV37twpU2tcXJx45513hBBCrF69WgwbNkyo1WoRHx8vXn75ZZGWlibUarXo27evuHjxooiLixP/93//J9LT04VOpxNTpkwRmzZtEpcuXRKvv/66yM/PF0IIMW/ePKFSqfTn8vDnwB+t748//hBBQUFCq9UKIYSYMWOG2Lhxo36fI0eOCCGEWLJkiViyZIkoKSkR3t7eIjU1VQghxPLly8W2bdvEN998IxYvXiyEEKKkpES888474uzZs8/UQyJjwyscRFTOsGHD8MUXX8Df3x/fffcdBg8eDLlcDgBYt24djh07hoyMDJw5c6bMj6q5u7uXm2vChAmIi4vDxo0bkZaWBrVajXv37gEA+vTpo79N8+abb+LMmTN4+eWX9fv+8ssv+P3337F69WoAgEajQWZmJl555ZXH1t6tWzeYm5vD0dERTZs2xUsvvQQAcHBwwO3btwEAHh4eaNOmDYAHa0127doFhUIBHx8f2NraAgBGjRqFWbNm6ed97bXXyh2rVatWCAkJwe7du5GRkYHffvsNL774ov7vD3/hs23btjh37hwuX74MBwcHff0Pr+hMnToVFy9eRFxcHIAHa2YuX76Mzp07P/Y8iUwNAwcRlePh4YE///wT2dnZOHDgANauXQvgwS9QoysMIAAAAr9JREFUjhgxAoMHD4aHhwfatWuH7du36/erV69eublUKhUyMzMxcOBAvP766/jll18g/vOLCo/+6nFFv4Ks0+mwdetW/a2dvLw82NnZVVq7QqHQ//txv6r8MDwBD37qWy6XQ6fTldlGCAGNRlPpuSUnJ+OTTz7B+PHj0a9fP5iZmenPDfj/7d2xSzphHMfx95FlYOAQSDRINhU1q6A0NASCQgoOBU6CNrh6kwgNLdYotLQFBU5NLYHY0BAh9AcIJrQ4FCI4yCk1/OD4geIix+8XfV7jHc/D873lPve9Bx5wu90AGIbB19cXi4uL9rHi8GcD6mAwYDweUywWOTg4AODz8xOPxzOzTpGfRptGRWSqw8NDLi8v8Xq99lf729sbhmFwcnJCKBTi4eGB8Xg8c56npyey2SyxWIx2u02327Vf7o+Pj/T7fYbDIff39xP7M8LhMDc3NwC0Wi0SiYTdHZlHs9m013F3d8fe3h7BYJB6vU6v1wOgVqsRCoUmxrpcLjuIvLy8EAwGOTo6YmNjg0ajMfN5BAIBPj4+aLVaAFxdXXF7e0s4HKZWq2FZFoPBgOPjY15fX+euU+R/og6HiEyVSqXY39/n7OzMvra1tcX29jaxWAzDMIhGozSbzZnz5PN5TNNkeXmZtbU1dnd3eX9/B2Bzc5NcLke/3ycejxONRu17AKVSiXK5TCKRAKBSqbCysjJ3bT6fD9M06Xa7RCIR0uk0CwsL5PN5MpkMlmWxs7PD6enpxNjV1VXW19fJZDJcXFxQKBTs9f1d2zRut5vz83NM08SyLPx+P5VKhaWlJTqdDslkktFoRCqVmhp2RH4ynRYrIr/K8/Mz1WqV6+vrf70UkV9Fv1RERETEcepwiIiIiOPU4RARERHHKXCIiIiI4xQ4RERExHEKHCIiIuI4BQ4RERFxnAKHiIiIOO4bP1OqJe6cHpUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(tuned_lightgbm, plot='feature')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qI_tk-8RzZu8"
   },
   "source": [
    "*Another* way to analyze the performance of models is to use the `evaluate_model()` function which displays a user interface for all of the available plots for a given model. It internally uses the `plot_model()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398,
     "referenced_widgets": [
      "2f905c9057e849cfb68d8b4a73a9ae2c",
      "4705ae97c5c341db8a7efcc7e4334c79",
      "202f5f0dfbea4f6cb6a75e4aebd370c7",
      "4c0b5f15356140e9bd6ed3a2e0850baa",
      "63e7b6e04e3d43118698abaca0973960",
      "b7731479f5f141289a4939cb73adfb28",
      "5eb7949ac4a140118e9ee6be49921284",
      "19ea86ac89a349e281011fbd327c29c0",
      "c9bbc67e75d1477e8cd7a64502b98704",
      "5334e09f4bed4a0ba45635300e76027e",
      "14244d0fca4f40a8b3552675869748a3",
      "baf068abf86f44a9a9502f79dfe17eb5",
      "3495c4e24f1d4b22af1117ea2403a4fa",
      "83a10da0205742d9a383671a0be02606",
      "4ae774bce02d4c1ab1351cd75b25cf17",
      "ee5416de1bb1462e87b2fa6f9065d646",
      "36a9c708b65e498586a07d526e6a5d06",
      "aeb87dce945f4d588807575e9f6e64c3"
     ]
    },
    "colab_type": "code",
    "id": "J4ryBACHzZu_",
    "outputId": "246ab553-8043-4cbe-fe99-7ea00bb13aed"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5620768d79fe4764929bc714d5ed12e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Hyperparameters', 'param…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CxKARgKAzZvJ"
   },
   "source": [
    "# 11.0 Predict on test / hold-out Sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r8k_rmplzZvL"
   },
   "source": [
    "Before finalizing the model, it is advisable to perform one final check by predicting the test/hold-out set and reviewing the evaluation metrics. If you look at the information grid in Section 6 above, you will see that 30% (1621 samples) of the data has been separated out as a test/hold-out sample. All of the evaluation metrics we have seen above are cross validated results based on training set (70%) only. Now, using our final trained model stored in the `tuned_lightgbm` variable we will predict the hold-out sample and evaluate the metrics to see if they are materially different than the CV results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ozTyeSjCzZvY",
    "outputId": "1cdf25f4-0988-40fa-c8e9-5695fec11f05"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>789.8498</td>\n",
       "      <td>2.985032e+06</td>\n",
       "      <td>1727.7244</td>\n",
       "      <td>0.9728</td>\n",
       "      <td>0.0841</td>\n",
       "      <td>0.0613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Model       MAE           MSE       RMSE      R2  \\\n",
       "0  Light Gradient Boosting Machine  789.8498  2.985032e+06  1727.7244  0.9728   \n",
       "\n",
       "    RMSLE    MAPE  \n",
       "0  0.0841  0.0613  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(tuned_lightgbm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UouOXHaxzZvo"
   },
   "source": [
    "The R2 on the test/hold-out set is **`0.9728`** compared to **`0.9753`** achieved on `tuned_lightgbm` CV results (in section 9.2 above). This is not a significant difference. If there is a large variation between the test/hold-out and CV results, then this would normally indicate over-fitting but could also be due to several other factors and would require further investigation. In this case, we will move forward with finalizing the model and predicting on unseen data (the 10% that we had separated in the beginning and never exposed to PyCaret).\n",
    "\n",
    "(TIP : It's always good to look at the standard deviation of CV results when using `create_model()`.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J0PmhEQFzZvr"
   },
   "source": [
    "# 12.0 Finalize Model for Deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rtaj0uWgzZvx"
   },
   "source": [
    "Model finalization is the last step in the experiment. A normal machine learning workflow in PyCaret starts with `setup()`, followed by comparing all models using `compare_models()` and shortlisting a few candidate models (based on the metric of interest) to perform several modeling techniques such as hyperparameter tuning, ensembling, stacking etc. This workflow will eventually lead you to the best model for use in making predictions on new and unseen data. The `finalize_model()` function fits the model onto the complete dataset including the test/hold-out sample (30% in this case). The purpose of this function is to train the model on the complete dataset before it is deployed in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UPk310pezZv0"
   },
   "outputs": [],
   "source": [
    "final_lightgbm = finalize_model(tuned_lightgbm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IGFUGDAPzZwF",
    "outputId": "ddaa33c4-f728-444a-fc33-47da5c37304a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LGBMRegressor(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
      "              importance_type='split', learning_rate=0.4, max_depth=10,\n",
      "              min_child_samples=20, min_child_weight=0.001, min_split_gain=0.9,\n",
      "              n_estimators=90, n_jobs=-1, num_leaves=10, objective=None,\n",
      "              random_state=123, reg_alpha=0.9, reg_lambda=0.2, silent=True,\n",
      "              subsample=1.0, subsample_for_bin=200000, subsample_freq=0)\n"
     ]
    }
   ],
   "source": [
    "#Final Light Gradient Boosting Machine parameters for deployment\n",
    "print(final_lightgbm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QmJjaIkrzZwQ"
   },
   "source": [
    "**Caution:** One final word of caution. Once the model is finalized using `finalize_model()`, the entire dataset including the test/hold-out set is used for training. As such, if the model is used for predictions on the hold-out set after `finalize_model()` is used, the information grid printed will be misleading as you are trying to predict on the same data that was used for modeling. In order to demonstrate this point only, we will use `final_knn` under `predict_model()` to compare the information grid with the one above in section 11. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bmYJRTAyzZwU",
    "outputId": "58e2f57b-abda-4166-c82b-af52ab6f18b8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>R2</th>\n",
       "      <th>RMSLE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Light Gradient Boosting Machine</td>\n",
       "      <td>582.5217</td>\n",
       "      <td>1221486.605</td>\n",
       "      <td>1105.2089</td>\n",
       "      <td>0.9889</td>\n",
       "      <td>0.0653</td>\n",
       "      <td>0.0493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Model       MAE          MSE       RMSE      R2  \\\n",
       "0  Light Gradient Boosting Machine  582.5217  1221486.605  1105.2089  0.9889   \n",
       "\n",
       "    RMSLE    MAPE  \n",
       "0  0.0653  0.0493  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_model(final_lightgbm);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5NkpL1ZHzZwr"
   },
   "source": [
    "Notice how the R2 in the `final_lightgbm` has increased to **`0.9889`** from **`0.9728`**, even though the model is same. This is because the `final_lightgbm` variable is trained on the complete dataset including the test/hold-out set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CgKSkSsZzZwv"
   },
   "source": [
    "# 13.0 Predict on unseen data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6n7QFM94zZwy"
   },
   "source": [
    "The `predict_model()` function is also used to predict on the unseen dataset. The only difference from section 11 above is that this time we will pass the `data_unseen` parameter. `data_unseen` is the variable created at the beginning of the tutorial and contains 10% (600 samples) of the original dataset which was never exposed to PyCaret. (see section 5 for explanation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YdlpJUx0zZw4",
    "outputId": "5b45a2b5-c9a1-4d20-80f7-28211f07d586"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
       "      <th>Report</th>\n",
       "      <th>Price</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.21</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>11572</td>\n",
       "      <td>10812.8943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>16775</td>\n",
       "      <td>15453.9273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>G</td>\n",
       "      <td>GIA</td>\n",
       "      <td>10429</td>\n",
       "      <td>10591.0952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.90</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4523</td>\n",
       "      <td>4639.1068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4375</td>\n",
       "      <td>4307.9166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight        Cut Color Clarity Polish Symmetry Report  Price  \\\n",
       "0          1.21      Ideal     G    VVS1     EX       EX    GIA  11572   \n",
       "1          2.00      Ideal     I     SI1     EX       VG    GIA  16775   \n",
       "2          1.51       Good     F     SI1     VG        G    GIA  10429   \n",
       "3          0.90      Ideal     F     SI1     EX       EX    GIA   4523   \n",
       "4          1.01  Very Good     I     SI1     VG       VG    GIA   4375   \n",
       "\n",
       "        Label  \n",
       "0  10812.8943  \n",
       "1  15453.9273  \n",
       "2  10591.0952  \n",
       "3   4639.1068  \n",
       "4   4307.9166  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unseen_predictions = predict_model(final_lightgbm, data=data_unseen)\n",
    "unseen_predictions.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wZnpuHoDzZxG"
   },
   "source": [
    "The `Label` column is added onto the `data_unseen` set. Label is the predicted value using the `final_lightgbm` model. If you want predictions to be rounded, you can use `round` parameter inside `predict_model()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "os2dbiIrzZxJ"
   },
   "source": [
    "# 14.0 Saving the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "46CV19RlzZxL"
   },
   "source": [
    "We have now finished the experiment by finalizing the `tuned_lightgbm` model which is now stored in `final_lightgbm` variable. We have also used the model stored in `final_lightgbm` to predict `data_unseen`. This brings us to the end of our experiment, but one question is still to be asked: What happens when you have more new data to predict? Do you have to go through the entire experiment again? The answer is no, PyCaret's inbuilt function `save_model()` allows you to save the model along with entire transformation pipeline for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tXl6hkG9zZxN",
    "outputId": "4af19b19-6c0c-4b1e-a4f7-249e729091bd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Succesfully Saved\n"
     ]
    }
   ],
   "source": [
    "save_model(final_lightgbm,'Final Lightgbm Model 08Feb2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q2wgdZ5ozZxX"
   },
   "source": [
    "(TIP : It's always good to use date in the filename when saving models, it's good for version control.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9LsyznpCzZxb"
   },
   "source": [
    "# 15.0 Loading the saved model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7ZH-4EMLzZxd"
   },
   "source": [
    "To load a saved model at a future date in the same or an alternative environment, we would use PyCaret's `load_model()` function and then easily apply the saved model on new unseen data for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2hsqdgn3zZxg",
    "outputId": "9db6b97b-ea93-452b-e4b4-121bea203bd3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformation Pipeline and Model Sucessfully Loaded\n"
     ]
    }
   ],
   "source": [
    "saved_final_lightgbm = load_model('Final Lightgbm Model 08Feb2020')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NBAXt62nzZx5"
   },
   "source": [
    "Once the model is loaded in the environment, you can simply use it to predict on any new data using the same `predict_model()` function. Below we have applied the loaded model to predict the same `data_unseen` that we used in section 13 above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "y7debJpCzZx8"
   },
   "outputs": [],
   "source": [
    "new_prediction = predict_model(saved_final_lightgbm, data=data_unseen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8hT1v3N0zZyD",
    "outputId": "c5f9994b-1859-49d0-9bb0-4d7f6c506b98"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Carat Weight</th>\n",
       "      <th>Cut</th>\n",
       "      <th>Color</th>\n",
       "      <th>Clarity</th>\n",
       "      <th>Polish</th>\n",
       "      <th>Symmetry</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.21</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>11572</td>\n",
       "      <td>10812.8943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.00</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>16775</td>\n",
       "      <td>15453.9273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.51</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>G</td>\n",
       "      <td>GIA</td>\n",
       "      <td>10429</td>\n",
       "      <td>10591.0952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.90</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>F</td>\n",
       "      <td>SI1</td>\n",
       "      <td>EX</td>\n",
       "      <td>EX</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4523</td>\n",
       "      <td>4639.1068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.01</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>SI1</td>\n",
       "      <td>VG</td>\n",
       "      <td>VG</td>\n",
       "      <td>GIA</td>\n",
       "      <td>4375</td>\n",
       "      <td>4307.9166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Carat Weight        Cut Color Clarity Polish Symmetry Report  Price  \\\n",
       "0          1.21      Ideal     G    VVS1     EX       EX    GIA  11572   \n",
       "1          2.00      Ideal     I     SI1     EX       VG    GIA  16775   \n",
       "2          1.51       Good     F     SI1     VG        G    GIA  10429   \n",
       "3          0.90      Ideal     F     SI1     EX       EX    GIA   4523   \n",
       "4          1.01  Very Good     I     SI1     VG       VG    GIA   4375   \n",
       "\n",
       "        Label  \n",
       "0  10812.8943  \n",
       "1  15453.9273  \n",
       "2  10591.0952  \n",
       "3   4639.1068  \n",
       "4   4307.9166  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_prediction.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cuVEPftKzZyK"
   },
   "source": [
    "Notice that the results of `unseen_predictions` and `new_prediction` are identical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uE3tuIUHzZyL"
   },
   "source": [
    "# 16.0 Wrap-up / Next Steps?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ct8oOjuvzZyS"
   },
   "source": [
    "This tutorial has covered the entire machine learning pipeline from data ingestion, pre-processing, training the model, hyperparameter tuning, prediction and saving the model for later use. We have completed all of these steps in less than 10 commands which are naturally constructed and very intuitive to remember such as `create_model()`, `tune_model()`, `compare_models()`. Re-creating the entire experiment without PyCaret would have taken well over 100 lines of code in most libraries.\n",
    "\n",
    "We have only covered the basics of `pycaret.regression`. In following tutorials we will go deeper into advanced pre-processing, ensembling, generalized stacking and other techniques that allow you to fully customize your machine learning pipeline and are must know for any data scientist.\n",
    "\n",
    "See you at the next tutorial. Follow the link to __[Regression Tutorial (REG102) - Level Intermediate](https://github.com/pycaret/pycaret/blob/master/Tutorials/Regression%20Tutorial%20Level%20Intermediate%20-%20REG102.ipynb)__"
   ]
  }
 ],
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kZ2fzyCOP4HQ6+eCDD5g2bVosXffFF19MJBLh9ttvZ/v27ei6zoMPPkiLFi34+eefYwsl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+W/+wMNqzYSWF9PzHaMm9e362u2Ri+lSneG8LqNPU9/TZNFD/6LI+/9WZev18/6tItBB4cZvG1l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F+Zz/6F3ofVAWv3D5jSx6+OmuYiLDpHbxp9wSHM1Vn73DWQ/+jjPvv52P7nuU1S/P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\ncJIIqlZ/jN16SUG03E1LgWMrZAdZqCkgZEjMSIR0j0GeR2K28sZ2HMVLD/6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        "metadata": {
         "tags": []
        },
        "output_type": "display_data",
        "text/plain": "<Figure size 576x396 with 2 Axes>"
       }
      ]
     }
    },
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     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
      "_view_module": "@jupyter-widgets/controls",
      "_view_module_version": "1.5.0",
      "_view_name": "ProgressView",
      "bar_style": "",
      "description": "Processing: ",
      "description_tooltip": null,
      "layout": "IPY_MODEL_baf068abf86f44a9a9502f79dfe17eb5",
      "max": 5,
      "min": 0,
      "orientation": "horizontal",
      "style": "IPY_MODEL_14244d0fca4f40a8b3552675869748a3",
      "value": 4
     }
    },
    "5eb7949ac4a140118e9ee6be49921284": {
     "model_module": "@jupyter-widgets/controls",
     "model_name": "IntProgressModel",
     "state": {
      "_dom_classes": [],
      "_model_module": "@jupyter-widgets/controls",
      "_model_module_version": "1.5.0",
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      "_view_count": null,
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      "_view_name": "ProgressView",
      "bar_style": "",
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